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Back to the Gradient Vector Flow Page
List of Citations from Science Citation Index for
C. Xu and J. L. Prince, ``Snakes, Shapes, and Gradient Vector Flow,'' IEEE
Transactions on Image Processing, 7(3): 359-369, March 1998.
web page
1998: 1 1999: 6 2000: 10 2001: 18 2002: 22 2003: 36 2004: 47 2005: 5
Total citations: 145
As of 11 Mar 2005
By Year - By Citation Rank - By Year with Abstract
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| 1998 |
- Xu, CY, and Prince, JL, "Generalized gradient vector flow external forces for active contours," SIGNAL PROCESSING, vol. 71, pp. 131-139, 1998.
Abstract:
Active contours, or snakes, are used extensively in computer vision and
image processing applications, particularly to locate object
boundaries. A new type of external force for active contours, called gi
adient vector flow (GVF) was introduced recently to address problems
associated with initialization and poor convergence to boundary
concavities. GVF is computed as a diffusion of the gradient vectors of
a gray-level or binary edge map derived from the image. In this paper,
we generalize the GVF formulation to include two spatially varying
weighting functions. This improves active contour convergence to long,
thin boundary indentations, while maintaining other desirable
properties of GVF, such as an extended capture range. The original GVF
is a special case of this new generalized GVF (GGVF) model. An error
analysis for active contour results on simulated test images is also
presented. (C) 1998 Elsevier Science B.V. All rights reserved.
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| 1999 |
- Kreho, A, Kehtarnavaz, N, Araabi, B, Hillman, G, Wursig, B, and Weller, D, "Assisting manual dolphin identification by computer extraction of dorsal ratio," ANNALS OF BIOMEDICAL ENGINEERING, vol. 27, pp. 830-838, 1999.
Abstract:
Marine biologists use a measurement called the "'Dorsal Ratio" in the
process of manual identification of bottlenose dolphins. The dorsal
ratio denotes the relative distances of the two largest notches from
the tip on the dorsal fin. The manual computation of this ratio is time
consuming, labor intensive, and user dependent. This paper presents a
computer-assisted system to extract the dorsal ratio for use in
identification of individual animals. The first component of the system
consists of active contour modeling where the trailing edge of the
dorsal fin is detected. This is followed by a curvature module to find
the characteristic fin points: tip and two most prominent notches.
Curvature smoothing is performed at various smoothing scales, and
wavelet coefficients are utilized to select an appropriate smoothing
scale. The dorsal ratio is then computed from the curvature function at
the appropriate: smoothing scale. The system was tested using 296
digitized images of dolphins, representing 94 individual dolphins. The
results obtained indicate that the computer extracted dorsal ratio can
be used in place of the manually extracted dorsal ratio as part of the
manual identification process. (C) 1999 cal Engineering Society.
[S0090-6964(99)00906-6].
- Chan, T, and Vese, L, "An active contour model without edges," SCALE-SPACE THEORIES IN COMPUTER VISION, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1682, pp. 141-151, 1999.
Abstract:
In this paper, we propose a new model for active contours to detect
objects in a given image, based on techniques of curve evolution,
Mumford-Shah functional for segmentation and level sets. Our model can
detect objects whose boundaries are not necessarily defined by
gradient. The model is a combination between more classical active
contour models using mean curvature motion techniques, and the
Mumford-Shah model for segmentation. We minimize an energy which can be
seen as a particular case of the so-called minimal partition problem.
In the level set formulation, the problem becomes a "mean-curvature
flow" -like evolving the active contour, which will stop on the desired
boundary. However, the stopping term does not depend on the gradient of
the image, as in the classical active contour models, but is instead
related to a particular segmentation of the image. Finally, we will
present various experimental results and in particular some examples
for which the classical snakes methods based on the gradient are not
applicable.
- Liang, JM, McInerney, T, and Terzopoulos, D, "Interactive medical image segmentation with United Snakes," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, MICCAI'99, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1679, pp. 116-127, 1999.
Abstract:
Snakes have become a standard image analysis technique with several
variants now in common use. We have developed a software package called
"United Snakes". It unifies the most important snake variants,
including finite difference, B-spline, and Hermite polynomial snakes,
within the framework of a general finite element formulation with a
choice of shape functions. Furthermore, we have incorporated into
united snakes a recently proposed snake-like technique known as
"livewire", via a method for imposing hard constraints on snakes. Here,
we demonstrate that the combination of techniques in united snakes
yields generality, accuracy, ease of use, and robustness in several
medical image analysis applications, including the segmentation of
neuronal dendrites in EM images, dynamic chest image analysis, and the
quantification of growth plates.
- Ma, TY, and Tagare, HD, "Consistency and stability of active contours with Euclidean and non-Euclidean arc lengths," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 8, pp. 1549-1559, 1999.
Abstract:
External energies of active contours are often formulated as Euclidean
are length integrals. In this paper, we show that such formulations are
biased. By this we mean that the minimum of the external energy does
not occur at an image edge. In addition, we also show that for certain
forms of external energy the active contour is unstable-when
initialized at the true edge, the contour drifts away and becomes
jagged. Both of these phenomena are due to the use of Euclidean are
length integrals.
We propose a non-Euclidean are length which eliminates these problems.
This requires a reformulation of active contours where a single
external energy function is replaced by a sequence of energy functions
and the contour evolves as an integral curve of the gradient of these
energies. The resulting active contour not only has unbiased external
energy, but is also more controllable,
Experimental evidence is provided in support of the theoretical claims.
- Xu, CY, Pham, DL, Rettmann, ME, Yu, DN, and Prince, JL, "Reconstruction of the human cerebral cortex from magnetic resonance images," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 18, pp. 467-480, 1999.
Abstract:
Reconstructing the geometry of the human cerebral cortex from MR images
is an important step in both brain mapping and surgical path planning
applications, Difficulties with imaging noise, partial volume
averaging, image intensity inhomogeneities, convoluted cortical
structures, and the requirement to preserve anatomical topology make
the development of accurate automated algorithms particularly
challenging. In this paper ne address each of these problems and
describe a systematic method for obtaining a surface representation of
the geometric central layer of the human cerebral cortex. Using fuzzy
segmentation, an isosurface algorithm, and a deformable surface model,
the method reconstructs the entire cortex with the correct topology,
including deep convoluted sulci and gyri. The method is largely
automated and its results are robust to imaging noise, partial volume
averaging, and image intensity inhomogeneities. The performance of this
method is demonstrated, both qualitatively and quantitatively and the
results of its application to sis subjects and one simulated MR brain
volume are presented.
- Chesnaud, C, Refregier, P, and Boulet, V, "Statistical region snake-based segmentation adapted to different physical noise models," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 21, pp. 1145-1157, 1999.
Abstract:
Algorithms for object segmentation are crucial in many image processing
applications. During past years, active contour models (snakes) have
been widely used for finding the contours of objects. This segmentation
strategy is classically edge-based in the sense that the snake is
driven to fit the maximum of an edge map of the scene. In this paper,
we propose a region snake approach and we determine fast algorithms for
the segmentation of an object in an image. The algorithms developed in
a Maximum Likelihood approach are based on the calculation of the
statistics of the inner and the outer regions (defined by the snake).
It has thus been possible to develop optimal algorithms adapted to the
random fields which describe the gray levels in the input image if we
assume that their probability density function family are known. We
demonstrate that this approach is still efficient when no boundary's
edge exists in the image. We also show that one can obtain fast
algorithms by transforming the summations over a region, for the
calculation of the statistics, into summations along the boundary of
the region. Finally, we will provide numerical simulation results for
different physical situations in order to illustrate the efficiency of
this approach.
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| 2000 |
- Pham, DL, Xu, CY, and Prince, JL, "Current methods in medical image segmentation," ANNUAL REVIEW OF BIOMEDICAL ENGINEERING, vol. 2, pp. 315-+, 2000.
Abstract:
Image segmentation plays a crucial role in many medical-imaging
applications, by automating or facilitating the delineation of
anatomical structures and other regions of interest. We present a
critical appraisal of the current status of semi-automated and
automated methods for the segmentation of anatomical medical images.
Terminology and important issues in image segmentation are first
presented. Current segmentation approaches are then reviewed with an
emphasis on the advantages and disadvantages of these methods for
medical imaging applications. We conclude with a discussion on the
future of image segmentation methods in biomedical research.
- Wu, HH, Liu, JC, and Chui, C, "A wavelet-frame based image force model for active contouring algorithms," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, pp. 1983-1988, 2000.
Abstract:
This paper proposes a directional image force (DIP) for active
contouring. DIF is the inner product of the zero crossing strength
(ZCS) of wavelet frame coefficients, and the normal of a snake. By
representing strength and orientation of edges at multiple resolution
levels, DIF markedly improves the immunity of snakes to noise and
convexity.
- Jones, TD, and Plassmann, P, "An active contour model for measuring the area of leg ulcers," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 19, pp. 1202-1210, 2000.
Abstract:
Leg ulcers are chronic skin wounds that affect many people and take a
long time to heal. The progress of wound healing and the effect of
clinical treatments can be monitored partly by measuring the area of
the wound. Measurements taken via manually based methods, such as using
a computer pointing device to delineate the wound boundary in a
digitized image, suffer from variations due to manual dexterity and
differences of opinion between observers. An active contour model is
presented that models the contour using piecewise B-spline arcs and
uses the minimax principle to adaptively regularize the contour
according to the local conditions in the wound image. The model makes
use of the existing manual delineation process in order to initialize
the solution and is shown to reduce the effect of the inherent
variations upon the repeatability and consistency of area measurements
in many cases.
- Chen, CM, and Lu, HHS, "An adaptive snake model for ultrasound image segmentation: Modified trimmed mean filter, ramp integration and adaptive weighting parameters," ULTRASONIC IMAGING, vol. 22, pp. 214-236, 2000.
Abstract:
The snake model is a widely-used approach to finding the boundary of
the object of interest in an ultrasound image. However, due to the
speckles, the weak edges and the tissue-related textures in an
ultrasound image, conventional snake models usually cannot obtain the
desired boundary satisfactorily. In this paper, we propose a new
adaptive snake model for ultrasound image segmentation. The proposed
snake model is composed of three major techniques, namely, the modified
trimmed mean (MTM) filtering, ramp integration and adaptive weighting
parameters. With the advantages of the mean and median filters, the MIM
filter is employed to alleviate the speckle interference in the
segmentation process. The weak edge enhancement by ramp integration
attempts to capture the slowly varying edges, which are hard to capture
by conventional snake models. The adaptive weighting parameter allows
weighting of each energy term to change adaptively during the
deformation process. The proposed snake model has been verified on the
phantom and clinical ultrasound images. The experimental results showed
that the proposed snake model achieves a reasonable performance with an
initial contour placed 10 to 20 pixels away from the desired boundary.
The mean minimal distances from the derived boundary to the desired
boundary have been shown to be less than 3.5 (for CNR greater than or
equal to 0.5) and 2.5 pixels, respectively, for the phantom and
ultrasound images.
- Kawata, Y, Niki, N, Ohmatsu, H, Kusumoto, M, Kakinuma, R, Mori, K, Nishiyama, H, Eguchi, K, Kaneko, M, and Moriyama, N, "Differential geometry based vector fields for characterizing surrounding structures of pulmonary nodules," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2000, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1935, pp. 348-357, 2000.
Abstract:
This paper presents a scheme to analyze surrounding structures of
pulmonary nodules by using differential geometry based vector fields.
In this scheme the differential characteristics such as the principal
curvatures and directions are computed from the differential values of
the isointensity surfaces, Each voxel in the nodule surrounding is
described in terms of shape index and curvedness derived from the
principal curvatures, Two vector fields are formed from the directions
of the maximum principal curvatures of nodule surrounding and gradient
vectors of nodule surface, respectively, The gradient vector field is
computed by diffusing the gradient vector on the nodule surface. The
regions corresponding to the cylindrical or conic figures which are
similar to vessel and plural images are segmented by the shape index
and curvedness values. Then, the relationship between the segmented
regions and the nodule is evaluated by the inner product of the
direction of the maximum principal curvature and the gradient vector.
We demonstrate the feasibility of the scheme to classify benign and
malignant nodules.
- Zhao, XC, and Qi, FH, "Automatic image segmentation based on deformable models and pixel affinity statistic," JOURNAL OF INFRARED AND MILLIMETER WAVES, vol. 19, pp. 33-37, 2000.
Abstract:
A general-purpose automatic image segmentation scheme that integrates
deformable models and region statistics was proposed. By using a
combination of image cues including intensity, gradient, color, and
texture rather a single source of information such as gradient
magnitude, and gradient vector flow (GVF) Snakes technology, this
scheme is able to automatically segment objects of an unknown number
and unknown locations in images. For reasonable segmentation results,
affine cell decomposition (ACD) technology was employed to
automatically merge models corresponding to the same object, while
split models corresponding to different objects, Experiments on
synthetic images with low signal-to-noise ratio images and nature
images show good performmance and robust of the approach, especially
it's capable to capture the discontinuous boundary and move snakes into
boundary concavities.
- Chen, CM, Lu, HHS, and Lin, YC, "An early vision-based snake model for ultrasound image segmentation," ULTRASOUND IN MEDICINE AND BIOLOGY, vol. 26, pp. 273-285, 2000.
Abstract:
Due to the speckles and the ill-defined edges of the object of
interest, the classic image-segmentation techniques are usually
ineffective in segmenting ultrasound (US) images. In this paper, we
present a new algorithm for segmenting general US images that is
composed of two major techniques; namely, the early-vision model and
the discrete-snake model, By simulating human early vision, the
early-vision model can capture both grey-scale and textural edges while
the speckle noise is suppressed. By performing deformation only on the
peaks of the distance map, the discrete-snake model promises better
noise immunity and more accurate convergence. Moreover, the constraint
for most conventional snake models that the initial contour needs to be
located very close to the actual boundary has been relaxed
substantially. The performance of the proposed snake model has been
shown to be comparable to manual delineation and superior to that of
the gradient vector flow (GVF) snake model. (C) 2000 World Federation
for Ultrasound in Medicine & Biology.
- Weerasinghe, C, Ji, L, and Yan, H, "A new method for ROI extraction from motion affected MR images based on suppression of artifacts in the image background," SIGNAL PROCESSING, vol. 80, pp. 867-881, 2000.
Abstract:
Patient motion during a magnetic resonance imaging (MRI) examination
causes ghost artifacts and blurring in the image. Object boundary
extraction from such a degraded image is a challenging task, especially
if the motion function of the object is unknown. Although there are
many algorithms presently available for solving segmentation tasks,
they can be easily misled by the ghost artifacts and blurring in the
background of the image. Therefore, we propose a two-step background
clearing algorithm, in order to facilitate the object boundary
extraction. The first step involves selection of the least motion
affected views, using an entropy minimization criterion for suppression
of motion induced blur. The second step involves cancellation of the
remaining ghost artifacts, using a fuzzy model representing the image
background region. Both the steps involved in background clearing tend
to increase the number of dark pixels in the image. The contour
extraction is performed using an active contour model (snake), which
was previously developed by the authors. The proposed method has been
applied to phantom data affected by severe rotational motion and to
spin-echo MR images, producing encouraging results. (C) 2000 Elsevier
Science B.V. All rights reserved.
- Figueiredo, MAT, Leitao, JMN, and Jain, AK, "Unsupervised contour representation and estimation using B-splines and a minimum description length criterion," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, pp. 1075-1087, 2000.
Abstract:
This paper describes a new approach to adaptive estimation of
parametric deformable contours based on B-spline representations. The
problem is formulated in a statistical framework with the likelihood
function being derived from a region-based image model. The parameters
of the image model, the contour parameters, and the B-spline
parameterization order (i.e., the number of control points) are all
considered unknown. The parameterization order is estimated via a
minimum description Length (MDL) type criterion. A deterministic
iterative algorithm is developed to implement the derived contour
estimation criterion. The result is an unsupervised parametric
deformable contour: it adapts its degree of smoothness/complexity
(number of control points) and it also estimates the observation
(image) model parameters. The experiments reported in the paper,
performed on synthetic and real (medical) images, confirm the adequacy
and good performance of the approach.
- Iannizzotto, G, and Vita, L, "Fast and accurate edge-based segmentation with no contour smoothing in 2-D real images," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 9, pp. 1232-1237, 2000.
Abstract:
In this paper we propose an edge-based segmentation algorithm built on
a new type of active contour which is fast, has a low computational
complexity and does not introduce unwanted smoothing on the retrieved
contours. The contours are always returned as closed chains of points,
resulting in a very useful base for subsequent shape representation
techniques.
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| 2001 |
- Ray, N, Chanda, B, and Das, J, "A fast and flexible multiresolution snake with a definite termination criterion," PATTERN RECOGNITION, vol. 34, pp. 1483-1490, 2001.
Abstract:
This paper. describes a fast process of parametric snake evolution with
a multiresolution strategy. Conventional parametric evolution method
relies on matrix inversion throughout the iteration intermittently, in
contrast the proposed method relaxes the matrix inversion which is:
costly and time consuming in cases where the resulting snake is
flexible. The proposed method also eliminates the input of snake
rigidity parameters when the snake is flexible. Also, a robust and
definite termination criterion for both conventional and proposed
methods is demonstrated ill this paper. (C) 2001 pattern Recognition
Society. Published by Elsevier Science Ltd. All rights reserved.
- Lie, WN, and Chuang, CH, "Fast and accurate snake model for object contour detection," ELECTRONICS LETTERS, vol. 37, pp. 624-626, 2001.
Abstract:
A new scheme in which a snake model is used fnr object contour
detection is proposed. By developing a no-search movement scheme,
accepting the effective gradient vector flow field as the contracting
force, and adjusting the weighting parameters automatically, an
algorithm that is fast, less sensitive to initial contour conditions
and accurate in approaching concave parts of an object boundary is
obtained.
- Chen, J, Qi, FH, and Cen, F, "3D image segment method with forecasting capability," JOURNAL OF INFRARED AND MILLIMETER WAVES, vol. 20, pp. 194-198, 2001.
Abstract:
A new 3D image segmentation algorithm was proposed. This algorithm
integrates the improved Active Contour model and a new forecast
algorithm. The forecast algorithm is based on the peculiarity of 3D
images i.e. the deformation of the contour along the space or time axis
is continuous in 3D images. The forecast algorithm analyzes the trend
of the deformation of the contour in the segmented images, then
prognosticates the location and the shape of the contour in the next
image. Experiments on medical anatomic images show that the new
algorithm can observably improve the accuracy of the segmentation and
reduce the rime needed.
- Ladak, HM, Thomas, JB, Mitchell, JR, Rutt, BK, and Steinman, DA, "A semi-automatic technique for measurement of arterial wall from black blood MRI," MEDICAL PHYSICS, vol. 28, pp. 1098-1107, 2001.
Abstract:
Black blood magnetic resonance imaging (MRI) has become a popular
technique fur imaging the artery wall in vivo. Its noninvasiveness and
high resolution make it ideal for studying the progression of early
atherosclerosis in normal volunteers or asymptomatic patients with mild
disease, However, the operator variability inherent in the manual
measurement of vessel wall area from MR images hinders the reliable
detection of relatively small changes in the artery wall over time. In
this paper we present a semi-automatic method for segmenting the inner
and outer boundary of the artery wall, and evaluate its operator
variability using analysis of variance (ANOVA). In our approach, a
discrete dynamic contour is approximately initialized by an operator,
deformed to the inner boundary, dilated, and then deformed to the outer
boundary. A group of four operators performed repeated measurements on
12 images from normal human subjects using both our semiautomatic
technique and a manual approach. Results from the ANOVA indicate that
the inter-operator standard error of measurement (SEM) of total wall
area decreased from 3.254 mm(2) (manual) to 1.293 mm(2) (semi
automatic), and the intra-operator SEM decreased from 3.005 mm(2) to
0.958 mm(2). Operator reliability coefficients increased fi om less
than 69% to more than 91% tinter-operator) and 95% (intra-operator).
The minimum detectable change in wall area improved from more than 8.32
mm(2) (intra-operator, manual) to less than 3.59 mm(2) tinter-operator,
semiautomatic), suggesting that it is better to have multiple operators
measure wall area with our semi-automatic technique than to have a
single operator make repeated measurements manually. Similar
improvements in wall thickness and lumen radius measurements were also
recorded. Since the semi-automatic technique has effectively ruled out
the effect. of the operator on these measurements, it may be possible
to use such techniques to expand prospective studies of atherogenesis
to multiple centers so as to increase access to real patient data
without sacrificing reliability. (C) 2001 American Association of
Physicists in Medicine.
- Kaygin, S, and Bulut, MM, "A new one-pass algorithm to detect region boundaries," PATTERN RECOGNITION LETTERS, vol. 22, pp. 1169-1178, 2001.
Abstract:
In this paper, active chain is introduced as a chain coded contour
whose shape is changed during iterations while it stays closed,
clockwise and 4 connected. The iterations of the proposed algorithm
move the chain items toward the interior region. This behaviour is
similar to the active contours (snakes). If the initial contour is
counter-clockwise, the same algorithm causes the active chain to expand
like a balloon and detect the inner boundaries of the regions. The
chain coded contours of all the separate regions can be detected in one
pass in O(NM) where N and M are the image dimensions in pixels. (C)
2001 Elsevier Science B.V. All rights reserved.
- He, SJ, Shen, XQ, Yang, YM, He, RJ, and Yan, WL, "Research on MRI brain segmentation algorithm with the application in model-based EEG/MEG," IEEE TRANSACTIONS ON MAGNETICS, vol. 37, pp. 3741-3744, 2001.
Abstract:
MRI head image segmentation is a key issue for real head and brain
construction In EEG/MEG applications. In the paper, methods for finding
brain and contours were presented, and real calculation models for EEG
and MEG research were constructed.
- Ongun, G, Halici, U, Leblebicioglu, K, Atalay, V, Beksac, S, and Beksac, M, "Automated contour detection in blood cell images by an efficient snake algorithm," NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, vol. 47, pp. 5839-5847, 2001.
Abstract:
The human cerebral cortex is topologically equivalent to a sheet and
can be considered topologically spherical if it is closed at the brain
stem. Low-level segmentation of magnetic resonance (MR) imagery
typically produces cerebral volumes whose tessellations are not
topologically spherical. We present a novel algorithm that analyzes and
constrains the topology of a volumetric object. Graphs are formed that
represent the connectivity of voxel segments in the foreground and
background of the image. These graphs are analyzed and minimal
corrections to the volume are made prior to tessellation. We apply the
algorithm to a simple test object and to cerebral white matter masks
generated by a low-level tissue identification sequence. We tessellate
the resulting objects using the marching cubes algorithm and verify
their topology by computing their Euler characteristics. A key benefit
of the algorithm is that it localizes the change to a volume to the
specific areas of its topological defects.
- Shattuck, DW, and Leahy, RM, "Automated graph-based analysis and correction of cortical volume topology," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 20, pp. 1167-1177, 2001.
Abstract:
The human cerebral cortex is topologically equivalent to a sheet and
can be considered topologically spherical if it is closed at the brain
stem. Low-level segmentation of magnetic resonance (MR) imagery
typically produces cerebral volumes whose tessellations are not
topologically spherical. We present a novel algorithm that analyzes and
constrains the topology of a volumetric object. Graphs are formed that
represent the connectivity of voxel segments in the foreground and
background of the image. These graphs are analyzed and minimal
corrections to the volume are made prior to tessellation. We apply the
algorithm to a simple test object and to cerebral white matter masks
generated by a low-level tissue identification sequence. We tessellate
the resulting objects using the marching cubes algorithm and verify
their topology by computing their Euler characteristics. A key benefit
of the algorithm is that it localizes the change to a volume to the
specific areas of its topological defects.
- Vemuri, BC, Guo, YL, and Wang, ZZ, "Deformable pedal curves and surfaces: Hybrid geometric active models for shape recovery," INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 44, pp. 137-155, 2001.
Abstract:
In this paper, we propose significant extensions to the "snake pedal"
model, a powerful geometric shape modeling scheme introduced in (Vemuri
and Guo, 1998). The extension allows the model to automatically cope
with topological changes and for the first time, introduces the concept
of a compact global shape into geometric active models. The ability to
characterize global shape of an object using very few parameters
facilitates shape learning and recognition. In this new modeling
scheme, object shapes are represented using a parameterized
function-called the generator-which accounts for the global shape of an
object and the pedal curve (surface) of this global shape with respect
to a geometric snake to represent any local detail. Traditionally,
pedal curves (surfaces) are defined as the loci of the feet of
perpendiculars to the tangents of the generator from a fixed point
called the pedal point. Local shape control is achieved by introducing
a set of pedal points-lying on a snake-for each point on the generator.
The model dubbed as a "snake pedal" allows for interactive manipulation
via forces applied to the snake. In this work, we replace the snake by
a geometric snake and derive all the necessary mathematics for evolving
the geometric snake when the snake pedal is assumed to evolve as a
function of its curvature. Automatic topological changes of the model
may be achieved by implementing the geometric snake in a level-set
framework. We demonstrate the applicability of this modeling scheme via
examples of shape recovery from a variety of 2D and 3D image data.
- Zhou, J, Xin, LP, Rong, G, and Zhang, D, "Algorithm of automatic cartridge identification," OPTICAL ENGINEERING, vol. 40, pp. 2860-2865, 2001.
Abstract:
An effective algorithm for automatic cartridge identification is
proposed. The ejector impression is used to calibrate the cartridge
image. Features of the firing pin impression and the breach face
impression are extracted using active snake and orientation analysis,
respectively. These different features are then integrated to make a
final decision by using a support vector machine. Experimental results
illustrate the effectiveness of our algorithm. (C) 2001 Society of
Photo-Optical Instrumentation Engineers.
- Dumitras, A, and Venetsanopoulos, AN, "Angular map-driven snakes with application to object shape description in color images," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 10, pp. 1851-1859, 2001.
Abstract:
We propose a method for shape description of objects in color images.
Our method employs angular maps in order to identify significant
changes of color within the image, which are then used to drive snake
models. To obtain an angular map, the angle values of the vectors
corresponding to color image pixels are first computed with respect to
a reference vector, and organized in a two-dimensional matrix. To
identify significant color changes within the original image, the edges
of the angular map are next extracted. The resulting edge map is then
presented to a snake model. Distance and gradient vector flow snake
models have been employed in this work. Experimental results show, not
only that the resulting object shape descriptions are accurate and
quite similar, but also that our method is computationally efficient
and flexible.
- Ferrant, M, Nabavi, A, Macq, B, Jolesz, FA, Kikinis, R, and Warfield, SK, "Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 20, pp. 1384-1397, 2001.
Abstract:
We present a new algorithm for the nonrigid registration of
three-dimensional magnetic resonance (MR) intraoperative image
sequences showing brain shift. The algorithm tracks key surfaces of
objects (cortical surface and the lateral ventricles) in the image
sequence using a deformable surface matching algorithm. The volumetric
deformation field of the objects is then inferred from the
displacements at the boundary surfaces using a linear elastic
biomechanical finite-element model. Two experiments on synthetic image
sequences are presented, as well as an initial experiment on
intraoperative MR images showing brain shift. The results of the
registration algorithm show a good correlation of the internal brain
structures after deformation, and a good capability of measuring
surface as well as subsurface shift. We measured distances between
landmarks in the deformed initial image and the corresponding landmarks
in the target scan. Cortical surface shifts of up to 10 min and
subsurface shifts of up to 6 mm were recovered with an accuracy of 1
nun or less and 3 min or less respectively.
- Hao, XH, Bruce, CJ, Pislaru, C, and Greenleaf, JF, "Segmenting high-frequency intracardiac ultrasound images of myocardium into infarcted, ischemic, and normal regions," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 20, pp. 1373-1383, 2001.
Abstract:
Segmenting abnormal from normal myocardium using high-frequency
intracardiac echocardiography (ICE) images presents new challenges for
image processing. Gray-level intensity and texture features of ICE
images of myocardium with the same structural/perfusion properties
differ. This significant limitation conflicts with the fundamental
assumption on which existing segmentation techniques are based. This
paper describes a new seeded region growing method to overcome the
limitations of the existing segmentation techniques. Three criteria are
used for region growing control: 1) Each pixel is merged into the
globally closest region in the multifeature space. 2) "Geographic
similarity" is introduced to overcome the problem that myocardial
tissue, despite having the same property (i.e., perfusion status), may
be segmented into several different regions using existing segmentation
methods. 3) "Equal opportunity competence" criterion is employed making
results independent of processing order. This novel segmentation method
is applied to in vivo intracardiac ultrasound images using pathology as
the reference method for the ground truth. The corresponding results
demonstrate that this method is reliable and effective.
- Chen, CM, Lu, HHS, and Hsiao, AT, "A dual-snake model of high penetrability for ultrasound image boundary extraction," ULTRASOUND IN MEDICINE AND BIOLOGY, vol. 27, pp. 1651-1665, 2001.
Abstract:
Most deformable models require the initial contour to be placed close
to the boundary of the object of interest for boundary extraction of
ultrasound (US) images, which is impractical in many clinical
applications. To allow a distant initial contour, a new dual-snake
model promising high penetrability through the interference of the
noises is proposed in this paper. The proposed dual-snake model
features a new far-reaching external force, called the discrete
gradient flow, a connected component-weighted image force, and an
effective stability evaluation of two underlying snakes. The
experimental results show that, with a distant initial contour, the
mean distance from the derived boundary to the desired boundary is less
than 1.4 pixels, and most snake elements are within 2.7 pixels of the
desired boundaries for the synthetic images with CNR greater than or
equal to 1. For the clinical US images, the mean distance is less than
1.9 pixels, and most snake elements are within 3 pixels of the desired
boundaries. (E-mail: chung@lotus.mc.ntu.edu.tw) (C) 2002 World
Federation for Ultrasound in Medicine Biology.
- Siddiqi, K, and Vasilevskiy, A, "3D flux maximizing flows," ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2134, pp. 636-650, 2001.
Abstract:
A number of geometric active contour and surface models have been
proposed for shape segmentation in the literature. The essential idea
is to evolve a curve (in 2D) or a surface (in 3D) so that it clings to
the features of interest in an intensity image. Several of these models
have been derived, using a variational formulation, as gradient flows
which minimize or maximize a particular energy functional. However, in
practice these models often fail on images of low contrast or narrow
structures. To address this problem we have recently proposed the idea
of maximizing the rate of increase of flux of an auxiliary vector field
through a curve. This has lead to an interpretation as a 2D gradient
flow, which is essentially parameter free. In this paper we extend the
analysis to 3D and prove that the form of the gradient flow does not
change. We illustrate its potential with level-set based segmentations
of blood vessels in a large 3D computed rotational angiography (CRA)
data set.
- Chan, TF, and Vese, LA, "Active contours without edges," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 10, pp. 266-277, 2001.
Abstract:
In this paper, we propose a new model for active contours to detect
objects in a given image, based on techniques of curve evolution,
Mumford-Shah functional for segmentation and level sets. Our model can
detect objects whose boundaries are not necessarily defined by
gradient. We minimize an energy which can he seen as a particular case
of the minimal partition problem, In the level set formulation, the
problem becomes a "mean-curvature flow"-like evolving the active
contour, which will stop on the desired boundary. However, the stopping
term does not depend on the gradient of the. image, as in the classical
active contour models, hut is instead related to a particular
segmentation of the image. We will give a numerical algorithm using
finite differences. Finally, we will present various experimental
results and in particular some examples for which the classical snakes
methods based on the gradient are not applicable. Also, the initial
curve can be anywhere in the image, and interior contours are
automatically detected.
- Kim, W, Lee, CY, and Lee, JJ, "Tracking moving object using Snake's jump based on image flow," MECHATRONICS, vol. 11, pp. 199-226, 2001.
Abstract:
An active contour model, Snake, was developed as a useful segmenting
and tracking tool for rigid or non-rigid (i.e, deformable) objects by
Kass in 1987. Snake is designed on the basis of Snake energies.
Segmenting and tracking can be executed successfully by the process of
energy minimization. The ability to contract is an important process
for segmenting objects from images, but the contraction forces of Kass'
Snake are dependent on the object's form. In this research, new
contraction energy, independent of the object's form, is proposed for
the better segmentation of objects. Kass' Snake can be applied to the
case of small changes between images because its solutions can be
achieved on the basis of variational approach. If a somewhat fast
moving object exists in successive images, Kass' Snake will not operate
well because the moving object may have large differences in its
position or form, between successive images. Snake's nodes may fall
into the local minima in their motion to the new positions of the
target object in next image. When the motion is too large to apply
image flow energy to tracking, a jump mode is proposed for solving the
problem. The vector used to make Snake's nodes jump to the new location
can be obtained by processing the image flow. The effectiveness of the
proposed Snake is confirmed by some simulations. (C) 2000 Published by
Elsevier Science Ltd.
- Kaspersen, JH, Lango, T, and Lindseth, F, "Wavelet-based edge detection in ultrasound images," ULTRASOUND IN MEDICINE AND BIOLOGY, vol. 27, pp. 89-99, 2001.
Abstract:
We introduce a new wavelet-based method for edge detection in
ultrasound (US) images. Each beam that is analyzed is first transformed
into the wavelet domain using the continuous wavelet transform (CWT).
Because the CWT preserves both scale and time information, it is
possible to separate the signal into a number of scales. The edge is
localized by first determining the scale at which the power spectrum,
based on the wavelet transform, has its maximum value. Next, at this
scale we find the position of the peak for the squared CWT. This method
does not depend on any threshold, after the range of scales have been
determined. We suggest a range of scales for US images in general.
Sample edge detections are demonstrated in US images of straight and
jagged edges of simple structures submerged in water bath, and of an
abdominal aorta aneurysm phantom. (E-mail:
Thomas.Lango@unimed.sintef.no) (C) 2001 World Federation for Ultrasound
in Medicine & Biology.
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| 2002 |
- Ji, LL, and Yan, H, "Loop-free snakes for highly irregular object shapes," PATTERN RECOGNITION LETTERS, vol. 23, pp. 579-591, 2002.
Abstract:
'Snakes' are an effective approach to image segmentation. However,
self-looping is a common problem that can cause segmentation failure of
snakes in the recovery of highly irregular object shapes, such as in
long tube-like shapes, sharp corners or deep concave/convex shapes.
This paper introduces the notion of loop-free snakes that can quickly
and effectively remove all self-loops during their evolution,
consistently deforming and conforming to complicated shapes of target
objects. The proposed snakes are less sensitive to their initial
contour condition, are resilient to their inconsistent parameter
settings in a certain degree and require low computing cost in terms of
both computation time and storage. Experiments are conducted to segment
real images with encouraging results. (C) 2002 Elsevier Science B.V.
All rights reserved.
- Dormann, D, Libotte, T, Weijer, CJ, and Bretschneider, T, "Simultaneous quantification of cell motility and protein-membrane-association using active contours," CELL MOTILITY AND THE CYTOSKELETON, vol. 52, pp. 221-230, 2002.
Abstract:
We present a new method for the quantification of dynamic changes in
fluorescence intensities at the cell membrane of moving cells. It is
based on an active contour method for cell-edge detection, which allows
tracking of changes in cell shape and position. Fluorescence
intensities at specific cortical subregions can be followed in space
and time and correlated with cell motility. The translocation of two
GFP tagged proteins (CRAC and GRP1) from the cytosol to the membrane in
response to stimulation with the chemoattractant cAMP during chemotaxis
of Dictyostelium cells and studies of the spatio-temporal dynamics of
this process exemplify the method: We show that the translocation can
be correlated with motility parameters and that quantitative
differences in the rate of association and dissociation from the
membrane can be observed for the two PH domain containing proteins. The
analysis of periodic CRAC translocation to the leading edge of a cell
responding to natural cAMP waves in a mound demonstrates the power of
this approach. It is not only capable of tracking the outline of cells
within aggregates in front of a noisy background, but furthermore
allows the construction of spatio-temporal polar plots, capturing the
dynamics of the protein distribution at the cell membrane within the
cells' moving co-ordinate system. Compilation of data by means of
normalised polar plots is suggested as a future tool, which promises
the so-far impossible practicability of extensive statistical studies
and automated comparison of complex spatio-temporal protein
distribution patterns.
- Chen, CM, Lu, HHS, and Huang, YS, "Cell-based dual snake model: A new approach to extracting highly winding boundaries in the ultrasound images," ULTRASOUND IN MEDICINE AND BIOLOGY, vol. 28, pp. 1061-1073, 2002.
Abstract:
Two common deficiencies of most conventional deformable models are the
need to place the initial contour very close to the desired boundary
and the incapability of capturing a highly winding boundary for
sonographic boundary extraction. To remedy these two deficiencies, a
new deformable model (namely, the cell-based dual snake model) is
proposed in this paper. The basic idea is to apply the dual snake model
in the cell-based deformation manner. While the dual snake model
provides an effective mechanism allowing a distant initial contour, the
cell-based deformation makes it possible to catch the winding
characteristics of the desired boundary. The performance of the
proposed cell-based dual snake model has been evaluated on synthetic
images with simulated speckles and on the clinical ultrasound (US)
images. The experimental results show that the mean distances from the
derived to the desired boundary points are 0.9 +/- 0.42 pixels and 1.29
+/- 0.39 pixels for the synthetic and the clinical US images,
respectively.
- Lee, YJ, and Lee, SY, "Geometric snakes for triangular meshes," COMPUTER GRAPHICS FORUM, vol. 21, pp. 229-+, 2002.
Abstract:
Feature detection is important in various mesh processing techniques,
such as mesh editing, mesh morphing, mesh compression, and mesh signal
processing. In spite of much research in computer vision, automatic
feature detection even for images still remains a difficult problem. To
avoid this dfficulty, semi-automatic or interactive techniques for
image feature detection have been investigated. In this paper, we
propose a geometric snake as an interactive tool for feature detection
on a 3D triangular mesh. A geometric snake is an extension of an image
snake, which is an active contour model that slithers from its initial
position specified by the user to a nearby feature while minimizing an
energy functional. To constrain the movement of a geometric snake onto
the surface of a mesh, we use the parameterization of the surrounding
region of a geometric snake. Although the definition of a feature may
vary among applications, we use the normal changes of faces to detect
features on a mesh. Experimental results demonstrate that geometric
snakes can successfully capture nearby features from user-specified
initial positions.
- Fan, Y, Jiang, TZ, and Evans, DJ, "Volumetric segmentation of brain images using parallel genetic algorithms," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 21, pp. 904-909, 2002.
Abstract:
Active model-based segmentation has frequently been used in medical
image processing with considerable success. Although the active
model-based method was initially viewed as an optimization problem,
most researchers implement it as a partial differential equation
solution. The advantages and disadvantages of the active model-based
method are distinct: speed and stability. To improve its performance, a
parallel genetic algorithm-based active model method is proposed and
applied to segment the lateral ventricles from magnetic resonance brain
images. First, an objective function is defined. Then one instance
surface was extracted using the finite-difference method-based active
model and used to initialize the first generation of a parallel genetic
algorithm. Finally, the parallel genetic algorithm is employed to
refine the result. We demonstrate that the method successfully
overcomes numerical instability and is capable of generating an
accurate and robust anatomic descriptor for complex objects in the
human brain, such as the lateral ventricles.
- Vasilevskiy, A, and Siddiqi, K, "Flux maximizing geometric flows," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 24, pp. 1565-1578, 2002.
Abstract:
Several geometric active contour models have been proposed for
segmentation in computer vision and image analysis. The essential idea
is to evolve a curve (in 2D) or a surface (in 3D) under constraints
from image forces so that it clings to features of interest in an
intensity image. Recent variations on this theme take into account
properties of enclosed regions and allow for multiple curves or
surfaces to be simultaneously represented. However, it is still unclear
how to apply these techniques to images of narrow elongated structures,
such as blood vessels, where intensity contrast may be low and reliable
region statistics cannot be computed. To address this problem, we
derive the gradient flows which maximize the rate of increase of flux
of an appropriate vector field through a curve (in 2D) or a surface (in
3D). The key idea is to exploit the direction of the vector field along
with its magnitude. The calculations lead to a simple and elegant
interpretation which is essentially parameter free and has the same
form in both dimensions. We illustrate its advantages with several
level-set-based segmentations of 2D and 3D angiography images of blood
vessels.
- Park, HK, and Chung, MJ, "External force of snake: virtual electric field," ELECTRONICS LETTERS, vol. 38, pp. 1500-1502, 2002.
Abstract:
Gradient vector flow (GVF) is an external force of snake that overcomes
traditional snake's problems: limited capture range and poor
convergence to concave boundaries. A new external force with the same
properties as the GVF is proposed. The proposed method has much shorter
computational time than the GVF.
- Fu, JC, Chai, JW, Wong, STC, Deng, JJ, and Yeh, JY, "De-noising of left ventricular myocardial borders in magnetic resonance images," MAGNETIC RESONANCE IMAGING, vol. 20, pp. 649-657, 2002.
Abstract:
In short axis left ventricular MR images, endocardial borders are the
major parameters in evaluation of cardiovascular functions such as end
diastolic volume, end systolic volume, and ejection fraction.
Functional analysis captures the dynamic behavior of the cardiovascular
system as revealed by the movement of the endocardial borders over
time. Because of the huge number of MR images, an effective
computerized tool is required for real time applications. One of the
widely used automatic border detection algorithm-dynamic programming-
generates zigzag borderlines, which lead to measurement errors. This
paper surveys the performance of the wavelet adaptive filter, the
snake, and the medial filter in smoothing over the zigzag borders
generated by dynamic programming. Statistical analysis of two hundred
and sixty four images from sixteen subjects show that all three
algorithms can reduce the border line errors in terms of Hausdorff
distance and border area error; however, only the wavelet adaptive
filter is effective in providing the physiological measurements such as
ejection fraction, end systolic volume and end diastolic volume. (C)
2002 Elsevier Science Inc. All rights reserved.
- Ilic, S, and Fua, P, "Using Dirichlet Free Form Deformation to fit deformable models to noisy 3-D data," COMPUTER VISION - ECCV 2002, PT II, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2351, pp. 704-717, 2002.
Abstract:
Free-form deformations (FFD) constitute an important geometric shape
modification method that has been extensively investigated for computer
animation and geometric modelling. In this work, we show that FFDs are
also very effective to fit deformable models to the kind of noisy 3-D
data that vision algorithms such as stereo tend to produce.
We advocate the use of Dirichlet Free Form Deformation (DFFD) instead
of more conventional FFDs because they give us the ability to place
control points at arbitrary locations rather than on a regular lattice,
and thus much greater flexibility. We tested our approach on stereo
data acquired from monocular video-sequences and show that it can be
successfully used to reconstruct a complex object such as the whole
head, including the neck and the ears, as opposed to the face only.
- Osareh, A, Mirmehdi, M, Thomas, B, and Markham, R, "Classification and localisation of diabetic-related eye disease," COMPUTER VISION - ECCV 2002, PT IV, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2353, pp. 502-516, 2002.
Abstract:
Retinal exudates are a characteristic feature of many retinal diseases
such as Diabetic Retinopathy. We address the development of a method to
quantitatively diagnose these random yellow patches in colour retinal
images automatically. After a colour normalisation and contrast
enhancement preprocessing step, the colour retinal image is segmented
using Fuzzy C-Means clustering. We then classify the segmented regions
into two disjoint classes, exudates and non-exudates, comparing the
performance of various classifiers. We also locate the optic disk both
to remove it as a candidate region and to measure its boundaries
accurately since it is a significant landmark feature for
ophthalmologists. Three different approaches are reported for optic
disk localisation based on template matching, least squares arc
estimation and snakes. The system could achieve an overall diagnostic
accuracy of 90.1% for identification of the exudate pathologies and
90.7% for optic disk localisation.
- Yezzi, A, and Tannenbaum, A, "4D active surfaces for cardiac analysis," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION-MICCAI 2002, PT 1, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2488, pp. 667-673, 2002.
Abstract:
In this note, we employ the geometric active contour models formulated
in [5,11,19] for edge detection and segmentation to temporal MR cardiac
images. The method is based on defining feature-based metrics on a
given image which leads to a snake paradigm in which the feature of
interest may be as the steady state of a curvature driven gradient
flow. The implementation of the flow is done without level sets. This
allow us to segment 4D sets directly, i.e., not as a series of 2D
slices or a temporal series of 3D volumes.
- Kawata, Y, Niki, N, Ohmatsu, H, and Moriyama, N, "Example-based assisting approach for pulmonary nodule classification in 3-D thoracic CT images," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION-MICCAI 2002, PT 1, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2488, pp. 793-800, 2002.
Abstract:
This paper describes an example-based assisting approach for
classifying pulmonary nodules in 3-D thoracic CT images. In this
approach the internal and surrounding structures of the nodule are
characterized by the distribution pattern of CT density and 3-D
curvature indexes. Each nodule is represented by means of a joint
histogram using the distance value from the nodule center. When given
an indeterminate nodule image, the images of lesions with known
diagnoses (e.g. malignant vs.benign) are retrieved from a 3-D nodule
image database. The malignant likelihood of the indeterminate case is
estimated by the difference between the representation patterns of the
indeterminate case and the retrieved lesions. In the present study, we
adopt the Mahalanobis distance as the difference measure and then,
explore the feasibility of the classification based on patterns of
similar lesion images.
- Rettmann, ME, Han, X, Xu, CY, and Prince, JL, "Automated sulcal segmentation using watersheds on the cortical surface," NEUROIMAGE, vol. 15, pp. 329-344, 2002.
Abstract:
The human cortical surface is a highly complex, folded structure.
Sulci, the spaces between the folds, define location on the cortex and
provide a parcellation into anatomically distinct areas. A topic that
has recently received increased attention is the segmentation of these
sulci from magnetic resonance images, with most work focusing on
extracting either the sulcal spaces between the folds or curve
representations of sulci. Unlike these methods, we propose a technique
that extracts actual regions of the cortical surface that surround
sulci, which we call "sulcal regions." The method is based on a
watershed algorithm applied to a geodesic depth measure on the cortical
surface. A well-known problem with the watershed algorithm is a
tendency toward oversegmentation, meaning that a single region is
segmented as several pieces. To address this problem, we propose a
postprocessing algorithm that merges appropriate segments from the
watershed algorithm. The sulcal regions are then manually labeled by
simply selecting the appropriate regions with a mouse click and a
preliminary study of sulcal depth is reported. Finally, a scheme is
presented for computing a complete parcellation of the cortical
surface. (C) 2002 Elsevier Science.
- Canero, C, Vilarino, F, Mauri, J, and Radeva, P, "Predictive (Un)distortion model and 3-D reconstruction by biplane snakes," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 21, pp. 1188-1201, 2002.
Abstract:
This paper is concerned with the three-dimensional (3-D) reconstruction
of coronary vessel centerlines and with how distortion of X-ray
angiographic images affects it. Angiographies suffer from pincushion
and other geometrical distortions, caused by the peripheral concavity
of the image intensifier (II) and the nonlinearity of electronic
acquisition devices. In routine clinical practice, where a
field-of-view (FOV) of 17-23 cm is commonly used for the acquisition of
coronary vessels, this distortion introduces a positional error of up
to 7 pixels for an image matrix size of 512 x 512 and an FOV of 17 cm.
This error increases with the size of the FOV. Geometrical distortions
have a significant effect on the validity of the 3-D reconstruction of
vessels from these images. We show how this effect can be reduced by
integrating a predictive model of (un)distortion into the biplane
snakes formulation for 3-D reconstruction. First, we prove that the
distortion can be accurately modeled using a polynomial for each view.
Also, we show that the estimated polynomial is independent of focal
length, but not of changes in anatomical angles, as the II is
influenced by the earth's magnetic field. Thus, we decompose the
polynomial into two components: the steady and the
orientation-dependent component. We determine the optimal polynomial
degree for each component, which is empirically determined to be five
for the steady component and three for the orientation-dependent
component. This fact simplifies the prediction of the
orientation-dependent polynomial, since the number of polynomial
coefficients to be predicted is lower. The integration of this model
into the biplane snakes formulation enables us to avoid image
unwarping, which deteriorates image quality and therefore complicates
vessel centerline feature extraction. Moreover, we improve the biplane
snake behavior when dealing with wavy vessels, by means of using
generalized gradient vector flow. Our experiments show that the
proposed methods in this paper decrease up to 88% the reconstruction
error obtained when geometrical distortion effects are ignored. Tests
on imaged phantoms and real cardiac images are presented as well.
- Ray, N, Acton, ST, and Ley, K, "Tracking leukocytes in vivo with shape and size constrained active contours," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 21, pp. 1222-1235, 2002.
Abstract:
Inflammatory disease is initiated by leukocytes (white blood cells)
rolling along the inner surface lining of small blood vessels called
postcapillary venules. Studying the number and velocity of rolling
leukocytes is essential to understanding and successfully treating
inflammatory diseases. Potential inhibitors of leukocyte recruitment
can be screened by leukocyte rolling assays and successful inhibitors
validated by intravital microscopy. In this paper, we present an active
contour or snake-based technique to automatically track the movement of
the leukocytes. The novelty of the proposed method lies in the energy
functional that constrains the shape and size of the active contour.
This paper introduces a significant enhancement over existing
gradient-based snakes in the form of a modified gradient vector flow.
Using the gradient vector flow, we can track leukocytes rolling at high
speeds that are not amenable to tracking with the existing edge-based
techniques. We also propose a new energy-based implicit sampling method
of the points on the active contour that replaces the computationally
expensive explicit method. To enhance the performance of this shape and
size constrained snake model, we have coupled it with Kalman filter so
that during coasting (when the leukocytes are completely occluded or
obscured), the tracker may infer the location of the center of the
leukocyte. Finally, we have compared the performance of the proposed
snake tracker with that of the correlation and centroid-based trackers.
The proposed snake tracker results in superior performance measures,
such as reduced error in locating the leukocyte under tracking and
improvements in the percentage of frames successfully tracked. For
screening and drug validation, the tracker shows promise as an
automated data collection tool.
- Zimmer, C, Labruyere, E, Meas-Yedid, V, Guillen, N, and Olivo-Marin, JC, "Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: A tool for cell-based drug testing," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 21, pp. 1212-1221, 2002.
Abstract:
This paper presents a segmentation and tracking method for quantitative
analysis of cell dynamics from in vitro videomicroscopy data. The
method is based on parametric active contours and includes several
adaptations that address important difficulties of cellular imaging,
particularly the presence of low-contrast boundary deformations known
as pseudopods, and the occurence of multiple contacts between cells.
First, we use an edge map based on the average intensity dispersion
that takes advantage of relative background homogeneity to facilitate
the detection of both pseudopods and interfaces between adjacent cells.
Second, we introduce a repulsive interaction between contours that
allows correct segmentation of objects in contact and overcomes the
shortcomings of previously reported techniques to enforce contour
separation. Our tracking technique was validated on a realistic data
set by comparison with a manually defined ground-truth and was
successfully applied to study the motility of amoebae in a biological
research project.
- Aleksic, PS, Williams, JJ, Wu, ZL, and Katsaggelos, AK, "Audio-visual speech recognition using MPEGA compliant visual features," EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, vol. 2002, pp. 1213-1227, 2002.
Abstract:
We describe an audio-visual automatic continuous speech recognition
system, which significantly improves speech recognition performance
over a wide range of acoustic noise levels, as well as under clean
audio conditions. The system utilizes facial animation parameters
(FAps) supported by the MPEG-4 standard for the visual representation
of speech. We also describe a robust and automatic algorithm we have
developed to extract FAPs from visual data, which does not require hand
labeling or extensive training procedures. The principal component
analysis (PCA) was performed on the FAPs in order to decrease the
dimensionality of the visual feature vectors, and the derived
projection weights were used as visual features in the audio-visual
automatic speech recognition (ASR) experiments. Both single-stream and
multistream hidden Markov models (HMMs) were used to model the ASR
system, integrate audio and visual information, and perform a
relatively large vocabulary (approximately 1000 words) speech
recognition experiments. The experiments performed use clean audio data
and audio data corrupted by stationary white Gaussian noise at various
SNRs. The proposed system reduces the word error rate (WER) by 20% to
23% relatively to audio-only speech recognition WERs, at various SNRs
(0-30 dB) with additive white Gaussian noise, and by 19% relatively to
audio-only speech recognition WER under clean audio conditions.
- Taton, B, and Lachaud, JO, "Deformable model with non-euclidean metrics," COMPUTER VISION - ECCV 2002 PT III, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2352, pp. 438-452, 2002.
Abstract:
Deformable models like snakes are a classical tool for image
segmentation. Highly deformable models extend them with the ability to
handle dynamic topological changes, and therefore to extract arbitrary
complex shapes. However, the resolution of these models largely depends
on the resolution of the image. As a consequence, their time and memory
complexity increases at least as fast as the size of input data. In
this paper we extend an existing highly deformable model, so that it is
able to locally adapt its resolution with respect to its position. With
this property, a significant precision is achieved in the interesting
parts of the image, while a coarse resolution is maintained elsewhere.
The general idea is to replace the Euclidean metric of the image space
by a deformed non-Euclidean metric, which geometrically expands areas
of interest. With this approach, we obtain a new model that follows the
robust framework of classical deformable models, while offering a
significant independence from both the size of input data and the
geometric complexity of image components.
- Yu, ZY, and Bajaj, C, "Normalized gradient vector diffusion and image segmentation," COMPUTER VISION - ECCV 2002 PT III, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2352, pp. 517-530, 2002.
Abstract:
In this paper, we present an approach for image segmentation, based on
the existing Active Snake Model and Watershed-based Region Merging. Our
algorithm includes initial segmentation using Normalized Gradient
Vector Diffusion (NGVD) and region merging based on Region Adjacency
Graph (RAG). We use a set of heat diffusion equations to generate a
vector field over the image domain, which provides us with a natural
way to define seeds as well as an external force to attract the active
snakes. Then an initial segmentation of the original image can be
obtained by a similar idea as seen in active snake model. Finally an
RAG-based region merging technique is used to find the true
segmentation as desired. The experimental results show that our
NGVD-based region merging algorithm overcomes some problems as seen in
classic active snake model. We will also see that our NGVD has several
advantages over the traditional gradient vector diffusion.
- Ji, LL, and Yan, H, "Attractable snakes based on the greedy algorithm for contour extraction," PATTERN RECOGNITION, vol. 35, pp. 791-806, 2002.
Abstract:
While most improved snakes were built under the original variational
scheme, this paper presents an attractable snake based on the greedy
snake (Williams and Shah, CVGIP: Image Understanding 55(1) (1992)
14-26). By use of a direct feedback mechanism that is seamlessly
consistent with the search strategy of the greedy algorithm, the
proposed approach is capable of inheriting the simplicity and
efficiency of that algorithm and performing competitively with related
snakes. To avoid undesirable local minima, an overall optimal edge
detector is designed. A suitable synthetic convergent criterion is
proposed which enables snakes to converge normally or oscillatingly on
target objects. An adaptive interpolation scheme that encourages snakes
to accurately sense the details of object shapes is also described.
This model is applied to extract contours from various images with
encouraging results. (C) 2002 Pattern Recognition Society. Published by
Elsevier Science Ltd. All rights reserved.
- Xu, D, Yuan, XH, Xia, LZ, and Yang, SZ, "Image segmentation method based on a deformable model," JOURNAL OF INFRARED AND MILLIMETER WAVES, vol. 21, pp. 49-53, 2002.
Abstract:
image segmentation method based on deformable model was presented. The
internal and external force fields were improved on the basis of a
balloon's force field. By controlling the direction of the force field,
Snakes were shrinked and split continuously, and at last the image was
segmented into different sections. The experiments of synthetic and
real images show that the method is effective.
- Chen, LF, Liao, HYM, and Lin, JC, "Wavelet-based optical flow estimation," IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 12, pp. 1-12, 2002.
Abstract:
In this paper, a new algorithm for accurate optical flow (OF)
estimation using discrete wavelet approximation is proposed. The
computation of OF depends on minimizing the image and smoothness
constraints. The proposed method takes advantages of the nature of
wavelet theory, which can efficiently and accurately approximate any
function. OF vectors and image functions are represented by means of
linear combinations of scaling basis functions. Based on such
wavelet-based approximation, the leading coefficients of these basis
functions carry global information about the approximated functions.
The proposed method can successfully convert the problem of minimizing
a constraint function into that of solving a linear system of a
quadratic and convex function of scaling coefficients. Once all the
corresponding coefficients are determined, the flow vectors can be
obtained accordingly. Experiments have been conducted on both synthetic
and real image sequences. In terms of accuracy, the results show that
our approach outperforms the existing methods which adopted the same
objective function as ours.
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| 2003 |
- Ho, GHP, and Shi, PC, "Boundary finding with curve embedding potential field," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2003, PT 2, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2879, pp. 721-729, 2003.
Abstract:
We introduce an implicit vector field representation for arbitrary
number of closed curves in space. Each curve-defining vector of the
Curve Embedding Potential Field (CEPF) is defined to be perpendicular
to the nearest curve, with its magnitude determined by its distance to
that curve. Thereafter, we present an image segmentation strategy
through the detection of the CEPF distortion caused by vector-form
image data constraints. The CEPF-based method allows grid-free
localization of curve elements at any detection stage, while
maintaining the advantages of being geometric in nature. Unlike the
level set methods, the evolution of the embedded curves is not
constrained in any particular directions, and the implementation is
straightforward. We discuss the accuracy and robustness of the
algorithms under different noise conditions, and present segmentation
results of various medical images, including MRI, ultrasound, and
mammogram. (1)
- McInerney, T, and Dehmeshki, H, "User-defined B-spline template-snakes," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2003, PT 2, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2879, pp. 746-753, 2003.
Abstract:
We combine a new user initialization process with a B-spline snake to
create a model with the properties of a deformable template. This
'template' snake can be constrained by its control polygon and is
initially extremely close to, and similar in shape to, the target
anatomical structure. The initialization process acts as almost a
pre-segmentation and labelling step, making the snake's task much
simpler and hence more likely to succeed in noisy images without
subsequent user editing. By imposing an order on the initialization
process, the user is able to transfer knowledge of global shape,
symmetry, landmark position etc. to the model. We apply our snake to
the segmentation of 2D medical images.
- Nascimento, JC, and Marques, JS, "An adaptive potential for robust shape estimation," IMAGE AND VISION COMPUTING, vol. 21, pp. 1107-1116, 2003.
Abstract:
This paper describes an algorithm for shape estimation in cluttered
scenes. A new image potential is defined based on strokes detected in
the image. The motivation is simple. Feature detectors (e.g. edge
points detectors) produce many outliers, which hamper the performance
of boundary extraction algorithms. To overcome this difficulty we
organize edges in strokes and assign a confidence degree (weight) to
each stroke. The confidence degrees depend on the distance of the
stroke points to the boundary estimates and they are updated during the
estimation process. A deformable model is used to estimate the object
boundary, based on the minimization of an adaptive potential function
which depends on the confidence degree assigned to each stroke.
Therefore, the image potential changes during the estimation process.
Both steps (weight update, energy minimization) are derived as the
solution of a maximum likelihood estimation problem using the EM
algorithm.
Experimental tests are provided to illustrate the performance of the
proposed algorithm. (C) 2003 Elsevier B.V. All rights reserved.
- Cen, F, and Qi, FH, "A new geometric active contour for medical image segmentation," JOURNAL OF INFRARED AND MILLIMETER WAVES, vol. 22, pp. 441-446, 2003.
Abstract:
Generally, the segmentation of a medical image is difficult, because
the medical image is often corrupted by norrupted by noise, and the
anatomical shape in the medical image is complicated. In this paper
presents a new geometric active contour scheme for medical image
segmentation. First, we regularize the attraction force field in the
geometric active contour model to extend the capture range of the
object boundaries, and improve the ability of convergence to the
concavities. Then, using a multi-scale scheme improve the boundary
detection accuracy. In addition, combining the regularization and the
multi-scale method, the proposed scheme can effectively suppress and
eliminate the noise and the spurious edges in the medical images.
Furthermore, the topology of the deforming curve can naturally change
without and special topolygy handing procedures added to the scheme.
This permits synchronously extracting several anatomical structures.
The experiments on some medical images obtained from different medical
imaging methods demonstrate that the proposed approach is competent for
medical image segmentation.
- Kawata, Y, Niki, N, Ohmatsu, H, and Moriyama, N, "Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images," ACADEMIC RADIOLOGY, vol. 10, pp. 1402-1415, 2003.
Abstract:
Rationale and Objectives. An example-based assisting approach that
supports decision making in classifying pulmonary nodules in
3-dimensional (3D) thoracic computed tomography images has been
developed.
Materials and Methods. The example-based assisting approach retrieves
and displays nodules that exhibit morphologic and internal profiles
consistent to the nodule in question. It uses a 3D computed tomography
image database containing 143 pulmonary nodules for which diagnosis is
known. The central module makes possible analysis of the query nodule
image and extraction of the features of interest: shape, surrounding
structure, and internal structure of the nodules. The principal axes
and the compactness characterize the nodule shape. The surrounding and
internal structures are represented by the distribution pattern of
computed tomography density value and 3D curvature indexes. The nodule
representation is then used for computing a similarity measure such as
a correlation coefficient and a malignant likelihood of the query
nodule. The malignant likelihood is estimated by the difference between
the representation patterns of the query case and the retrieved
lesions. The Mahalanobis distance was adopted as the difference
measure. The approach performance was assessed through leave-one-out
method by the false-positive rate.
Results. Given a query nodule image, the proposed method retrieved
benign and malignant images similar to the query case and provided its
malignant likelihood. The number of cases that obtained enough number
of the retrieved cases for estimating the malignant likelihood was 107
cases (malignant, 70; benign, 37) in our database. Sensitivity was
91.4% (64 of 70 malignant nodules), specificity was 51.4% (19 of 37
benign nodules), and accuracy values were 77.6% (83 of 107 nodules).
Conclusion. Preliminary assessment of this approach showed that an
example-based assisting approach is an effective tool for making the
diagnostic decision in the classification of pulmonary nodules using
the nodule image database.
- Bajaj, C, Yu, ZY, and Auer, M, "Volumetric feature extraction and visualization of tomographic molecular imaging," JOURNAL OF STRUCTURAL BIOLOGY, vol. 144, pp. 132-143, 2003.
Abstract:
Electron tomography is useful for studying large macromolecular complex
within their cellular context. The associate problems include crowding
and complexity. Data exploration and 3D visualization of complexes
require rendering of tomograms as well as extraction of all features of
interest. We present algorithms for fully automatic boundary
segmentation and skeletonization, and demonstrate their applications in
feature extraction and visualization of cell and molecular tomographic
imaging. We also introduce an interactive volumetric exploration and
visualization tool (Volume Rover), which encapsulates implementations
of the above volumetric image processing algorithms, and additionally
uses efficient multi-resolution interactive geometry and volume
rendering techniques for interactive visualization. (C) 2003 Elsevier
Inc. All rights reserved.
- Perrin, DP, Kadioglu, E, Stoeter, SA, and Papanikolopoulos, N, "Grasping and tracking using constant curvature dynamic contours," INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, vol. 22, pp. 855-871, 2003.
Abstract:
In this paper we present our constant curvature dynamic contours
(snakes) and three applications of these: visual servoing and grasping,
occluding contour depth extraction, and localization of miniature
mobile robots. For the first application, a novel deformable contour
model is implemented for the automatic determination of plausible grasp
axes of unknown objects using an eye-in-hand robotic system. The
si-stem finds potential grasp point pairs, ranks them based upon
measurements taken from the contour, and executes a vision-guided grasp
using the highest ranked grasp point pair to determine the gripper
alignment. Our method is based upon statistical active deformable
models. We have developed a new snake model that is applicable to
real-time vision problems. The grasping method is experimentally
verified using both simple and complex unknown grasping targets. These
experiments demonstrate the effectiveness of using the proposed snakes
to grasp previously unknown objects in minimally structured
environments. We also present a novel method for active monocular depth
recovery (second application of our snakes). It combines new, highly
stable active deformable models with a structured camera motion along
the optical axis to produce depth estimates for all snake control
points. The method has a simple formulation and is suitable for
real-time, vision-based robotic applications. Experiments with a
variety of objects and depths demonstrate the practicality of the
method. Finally, we present a novel method for localizing miniature
mobile robots (Scouts) using dynamic contours. The miniature robot is
tracked as it moves and jumps in the environment. The proposed dynamic
contours art, very effective in tracking the fast accelerations and
decelerations of this small robot. We show initial experimental results
emphasizing the task of monitoring a Scout's jumps.
- Hanek, R, Schmitt, T, Buck, S, and Beetz, M, "Towards RoboCup without color labeling," ROBOCUP 2002: ROBOT SOCCER WORLD CUP VI, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, vol. 2752, pp. 179-194, 2003.
Abstract:
Object recognition and localization methods in RoboCup work on color
segmented camera images. Unfortunately, color labeling can be applied
to object recognition tasks only in very restricted environments, where
different kinds of objects have different colors. To overcome these
limitations we propose an algorithm named the Contracting Curve Density
(CCD) algorithm for fitting parametric curves to image data. The method
neither assumes object specific color distributions, nor specific edge
profiles, nor does it need threshold parameters. Hence, no training
phase is needed. In order to separate adjacent regions we use local
criteria which are based on local image statistics. We apply the method
to the problem of localizing the ball and show that the CCD algorithm
reliably localizes the ball even in the presence of heavily changing
illumination, strong clutter, specularity, partial occlusion, and
texture.
- Hsu, RL, and Jain, AK, "Generating discriminating cartoon faces using interacting snakes," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 25, pp. 1388-1398, 2003.
Abstract:
As a computational bridge between the high-level a priori knowledge of
object shape and the low-level image data, active contours (or snakes)
are useful models for the extraction of deformable objects. We propose
an approach for manipulating multiple snakes iteratively, called
interacting snakes, that minimizes the attraction energy functionals on
both contours and enclosed regions of individual snakes and the
repulsion energy functionals among multiple snakes that interact with
each other. We implement the interacting snakes through explicit curve
(parametric active contours) representation in the domain of face
recognition. We represent human faces semantically via facial
components such as eyes, mouth, face outline, and the hair outline.
Each facial component is encoded by a closed (or open) snake that is
drawn from a 3D generic face model. A collection of semantic facial
components form a hypergraph, called semantic face graph, which employs
interacting snakes to align the general facial topology onto the sensed
face images. Experimental results show that a successful interaction
among multiple snakes associated with facial components makes the
semantic face graph a useful model for face representation, including
cartoon faces and caricatures, and recognition.
- Yezzi, AJ, and Prince, JL, "An Eulerian PDE approach for computing tissue thickness," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 22, pp. 1332-1339, 2003.
Abstract:
We outline an Eulerian framework for computing the thickness of tissues
between two simply connected boundaries that does not require landmark
points or parameterizations of either boundary. Thickness is defined as
the length of correspondence trajectories, which run from one tissue
boundary to the other, and which follow a smooth vector field
constructed in the region between the boundaries. A pair of partial
differential equations (PDEs) that are guided by this vector field are
then solved over this region, and the sum of their solutions yields the
thickness of the tissue region. Unlike other approaches, this approach
does not require explicit construction of any correspondence
trajectories. An efficient, stable, and computationally fast solution
to these PDEs is found by careful selection of finite differences
according to an upwinding condition. The behavior and performance of
our method is demonstrated on two simulations and two magnetic
resonance imaging data sets in two and three dimensions. These
experiments reveal very good performance and show strong potential for
application in tissue thickness visualization and quantification.
- Pardo, XM, Radeva, P, and Cabello, D, "Discriminant snakes for 3D reconstruction of anatomical organs," MEDICAL IMAGE ANALYSIS, vol. 7, pp. 293-310, 2003.
Abstract:
In this work a new statistic deformable model for 3D segmentation of
anatomical organs in medical images is proposed. A statistic
discriminant snake performs a supervised learning of the object
boundary in an image slice to segment the next slice of the image
sequence. Each part of the object boundary is projected in a feature
space generated by a bank of Gaussian filters. Then, clusters
corresponding to different boundary pieces are constructed by means of
linear discriminant analysis. Finally, a parametric classifier is
generated from each contour in the image slice and embodied into the
snake energy-minimization process to guide the snake deformation in the
next image slice. The discriminant snake selects and classifies image
features by the parametric classifier and deforms to minimize the
dissimilarity between the learned and found image features. The new
approach is of particular interest for segmenting 3D images with
anisotropic spatial resolution, and for tracking temporal image
sequences. In particular, several anatomical organs from different
imaging modalities are segmented and the results compared to expert
tracings. (C) 2003 Elsevier B.V. All rights reserved.
- Makela, T, Pham, QC, Clarysee, P, Nenonen, J, Lotjonen, J, Sipila, O, Hanninen, H, Lauerma, K, Knuti, J, Katila, T, and Magnin, IE, "A 3-D model-based registration approach for the PET, MR and MCG cardiac data fusion," MEDICAL IMAGE ANALYSIS, vol. 7, pp. 377-389, 2003.
Abstract:
In this paper, a new approach is presented for the assessment of a 3-D
anatomical and functional model of the heart including structural
information from magnetic resonance imaging (MRI) and functional
information from positron emission tomography (PET) and
magnetocardiography (MCG). The method uses model-based co-registration
of MR and PET images and marker-based registration for MRI and MCG.
Model-based segmentation of MR anatomical images results in an
individualized 3-D biventricular model of the heart including
functional parameters from PET and MCG in an easily interpretable 3-D
form. (C) 2003 Elsevier B.V. All rights reserved.
- Wang, X, He, L, Tang, YJ, and Wee, WG, "A divide and conquer deformable contour method with a model based searching algorithm," IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, vol. 33, pp. 738-751, 2003.
Abstract:
A divide and conquer deformable contour method is presented with an
initial inside closed contour being divided into arbitrary segments,
and these segments are allowed to deform separately preserving the
segments' connectivity. A maximum area threshold, A a., is used to stop
these outward contour segments' marching. Clear and blur contour points
are then identified to partition the whole contour into clear and blur
segments. A bi-directional searching method is then recursively applied
to each blur segment including a search for contour-within-contour
segment to reach a final close contour. Further improvements are
provided by a model based searching algorithm. It is a two-step process
with step 1 being a linked contour model matching operation where
landmarks are extracted, and step 2 being a posteriori probability
model matching and correction operation where large error segments are
fine tuned to obtain the final results. The experiments include
ultrasound images of pig heart, MRI brain images, MRI knee images
having complex shapes with or without gaps, and inhomogeneous interior
and contour region brightness distributions. These experiments have
shown that the method has the capability of moving a contour into the
neighboring region of the desired boundary by overcoming inhomogeneous
interior, and by adapting each contour segment searching operation to
different local difficulties, through a contour partition and
repartition scheme in searching for a final solution.
- Mohanna, F, and Mokhtarian, F, "An efficient active contour model through curvature scale space filtering," MULTIMEDIA TOOLS AND APPLICATIONS, vol. 21, pp. 225-242, 2003.
Abstract:
Active contour models can be successfully used in multimedia database
retrieval systems if they have good accuracy and high speed. The
majority of existing active contour models do not lock on to interest
objects very accurately and quickly especially in complex images. The
behavior of the active contour is generally controlled by its internal
and external energies. Internal energy is composed of two parts; the
first part acts to shorten the active contour as it iterates towards
the interest object, while the second part is the curvature of the
active contour and forces smoothness of active contour during its
movement towards interest object. In this paper, first a reformulated
internal energy is proposed to improve the computation of curvature at
point vi by making use of the three points v(i-1), v(i) and v(i+1).
Second, an accurate and high speed active contour model, SAC is
proposed based on reformulating internal energy by removing the
curvature part and using Gaussian filtering with low scale of
smoothing. The SAC model has only one parameter that affects the
internal energy of active contour and as a result of using the
Curvature Scale Space (CSS)(1) technique for smoothing, the SAC model
is more independent of model parameter setting and the initial snake.
- Chang, RF, Wu, WJ, Tseng, CC, Chen, DR, and Moon, WK, "3-D snake for US in margin evaluation for malignant-breast tumor excision using mammotome," IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, vol. 7, pp. 197-201, 2003.
Abstract:
The goal of this paper is to use the three-dimensional (3-D) snake
technique in 3-D ultrasound to obtain the tumor contour for the pre-
and the post-operative malignant breast excision by the vacuum assisted
biopsy instrument Mammotome. This technique of assessing the margin of
two can help the physician to evaluate the effect of the surgery. By
using the anisotropic diffusion filter, the noise and speckles can be
reduced. Then the stick detection is adopted for enhancing the edge.
Finally, the gradient vector flow (GVF) snake is used to obtain the
tumor contour. These techniques are extended to the 3-D techniques to
increase the accuracy and robust of segmentation results. We hope that
this study can help physicians to improve the minimal invasive
operation for a breast tumor.
- Langs, G, Peloschek, P, and Bischof, H, "ASM driven snakes in rheumatoid arthritis assessment," IMAGE ANALYSIS, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2749, pp. 454-461, 2003.
Abstract:
In this paper a method is proposed that combines active shape models
(ASM) and active contours (snakes) in order to identify fine structured
contours with high accuracy and stability. Based on an estimate of the
contour position by an active shape model the accuracy of the landmarks
and the contour in between is enhanced by applying an iterative active
contour algorithm to a set of gray value profiles extracted
orthogonally to the interpolation obtained by the ASM. The active shape
model is trained with a set of training shapes, whereas the snake
detects the contour with fewer constraints. This is of particular
importance for the assessment of pathological changes of bones like
erosive destructions caused by rheumatoid arthritis.
- Karlsson, A, Strahlen, K, and Heyden, A, "Segmentation of histopathological section using snakes," IMAGE ANALYSIS, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2749, pp. 595-602, 2003.
Abstract:
This paper presents a semi-automatic method for Segmentation of digital
images. The segmentation method is based on snakes and a novel
implementation of the snake evolution algorithm is presented.
Analytical expressions describing the snake evolution axe derived using
the Fourier transform. These expressions can be sampled and used in a
fast algorithm for snake propagation. Experiments are carried out on
images of histopathological tissue sections and the results are very
promising. In particular the method is able to cope with overlapping
nuclei.
- Kammerer, P, Langs, G, Sablatnig, R, and Zolda, E, "Stroke segmentation in infrared reflectograms," IMAGE ANALYSIS, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2749, pp. 1138-1145, 2003.
Abstract:
An algorithm for the automatic segmentation of strokes in underdrawings
- the basic concept of the artist - in ancient panel paintings is
presented. The purpose of the stroke analysis is the determination of
the drawing tool used to draft the painting. This information allows
significant support for a systematic stylistic approach in the analysis
of paintings. Up to now, this analysis has been made by naked eye
examination only, and the restricted human optical retentiveness
complicated the comparison of different underdrawings with respect to
drawing tools and stroke characteristics. Stroke segmentation in
painting is related to the extraction and recognition of handwritings,
therefore similar techniques to segment the strokes from the background
incorporating boundary information are used. Following the
segmentation, the approximation of the stroke boundary by a closed
polygon done based on active contours. Results of the algorithms
developed are presented for both test panels and real reflectograms.
- Karlsson, A, Strahlen, K, and Heyden, A, "A fast snake segmentation method applied to histopathological sections," ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2683, pp. 261-274, 2003.
Abstract:
Using snakes to segment images has proven to be a powerful tool in many
different applications. The snake is usually propagated by minimizing
an energy function. The standard way of updating the snake from the
energy function is time consuming. This paper presents a fast snake
evolution algorithm, based on a more efficient numeric scheme for
updating the snake. Instead of inverting a matrix derived from
approximating derivatives in a sampled snake, an analytical expression
is obtained. The expression takes the form of a convolution with a
filter given by an explicit formula. The filter function can then be
sampled and used to propagate snakes in a fast and straightforward
manner. The proposed method is generally applicable to snakes and is
here used for propagating snakes in a gradient vector flow field.
Experiments axe carried out on images of histopathological tissue
sections and the results axe very promising.
- Gil, D, and Radeva, P, "Curvature vector flow to assure convergent deformable models for shape modelling," ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2683, pp. 357-372, 2003.
Abstract:
Poor convergence to concave shapes is a main limitation of snakes as a
standard segmentation and shape modelling technique. The gradient of
the external energy of the snake represents a force that pushes the
snake into concave regions, as its internal energy increases when new
inflexion points are created. In spite of the improvement of the
external energy by the gradient vector flow technique, highly non
convex shapes can not be obtained, yet. In the present paper, we
develop a new external energy based on the geometry of the curve to be
modelled. By tracking back the deformation of a curve that evolves by
minimum curvature flow, we construct a distance map that encapsulates
the natural way of adapting to non convex shapes. The gradient of this
map, which we call curvature vector flow (CVF), is capable of
attracting a snake towards any contour, whatever its geometry. Our
experiments show that, any initial snake condition converges to the
curve to be modelled in optimal time.
- Galland, F, Bertaux, N, and Refregier, P, "Minimum description length synthetic aperture radar image segmentation," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 12, pp. 995-1006, 2003.
Abstract:
We present a new minimum description length (MDL) approach based on a
deformable partition-a polygonal grid-for automatic segmentation of
speckled image composed of several homogeneous regions. The image
segmentation thus consists in the estimation of the polygonal grid, or,
more precisely, its number of regions, its number of nodes and the
location of its nodes. These estimations are performed by minimizing a
unique MDL criterion which takes into account the probabilistic
properties of speckle fluctuations and a measure of the stochastic
complexity of the polygonal grid. This approach then leads to a global
MDL criterion without undetermined parameter since no other
regularization term than the stochastic complexity of the polygonal
grid is necessary and noise parameters can be estimated with maximum
likelihood-like approaches. The performance of this technique is
illustrated on synthetic and real Synthetic Aperture Radar images of
agricultural regions and the influence of different terms of the model
is analyzed.
- Feng, GC, and Jiang, JM, "Image segmentation in compressed domain," JOURNAL OF ELECTRONIC IMAGING, vol. 12, pp. 390-397, 2003.
Abstract:
We propose a direct image segmentation algorithm in the JPEG compressed
domain. The algorithm features extracting statistical parameters from
direct cosine transform (DCT) coefficients without its inverse
transform, and growing regions in line with JPEG compression seamlessly
in blocks of 8 x 8 pixels. In comparison with the latest research
efforts in region-based image segmentation, our proposed algorithm
achieves significant advantages, including (1) no iteration is
involved, (2) no full decompression is required, and (3) segmentation
performance is competitive. (C) 2003 SPIE and IST.
- Positano, V, Santarelli, MF, and Landini, L, "Automatic characterization of myocardial perfusion in contrast enhanced MRI," EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, vol. 2003, pp. 413-421, 2003.
Abstract:
The use of contrast medium in cardiac MRI allows joining the
high-resolution anatomical information provided by standard magnetic
resonance with functional information obtained by means of the
perfusion of contrast agent in myocardial tissues. The current approach
to perfusion MRI characterization is the qualitative one, based on
visual inspection of images. Moving to quantitative analysis requires
extraction of numerical indices of myocardium perfusion by analysis of
time/intensity curves related to the area of interest. The main problem
in quantitative image sequence analysis is the heart movement, mainly
due to patient respiration. We propose an automatic procedure based on
image registration, segmentation of the myocardium, and extraction and
analysis of time/intensity curves. The procedure requires a minimal
user interaction, is robust with respect to the user input, and allows
effective characterization of myocardial perfusion. The algorithm was
tested on cardiac MR images acquired from voluntaries and in clinical
routine.
- Han, X, Xu, CY, and Prince, JL, "A topology preserving level set method for geometric deformable models," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 25, pp. 755-768, 2003.
Abstract:
Active contour and surface models, also known as deformable models, are
powerful image segmentation techniques. Geometric deformable models
implemented using level set methods have advantages over parametric
models due to their intrinsic behavior, parameterization independence,
and ease of implementation. However, a long claimed advantage of
geometric deformable models-the ability to automatically handle
topology changes-turns out to be a liability in applications where the
object to be segmented has a known topology that must be preserved. In
this paper, we present a new class of geometric deformable models
designed using a novel topology-preserving level set method, which
achieves topology preservation by applying the simple point concept
from digital topology. These new models maintain the other advantages
of standard geometric deformable models including subpixel accuracy and
production of nonintersecting curves or surfaces. Moreover, since the
topology-preserving constraint is enforced efficiently through local
computations, the resulting algorithm incurs only nominal computational
overhead over standard geometric deformable models. Several experiments
on simulated and real data are provided to demonstrate the performance
of this new deformable model algorithm.
- Doulamis, A, Doulamis, N, Ntalianis, K, and Kollias, S, "An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier architecture," IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 14, pp. 616-630, 2003.
Abstract:
In this paper, an unsupervised video object (VO) segmentation and
tracking algorithm is proposed based on an adaptable neural-network
architecture. The proposed scheme comprises: 1) a VO tracking module
and) an initial VO estimation module. Object tracking is handled as a
classification problem and implemented through an adaptive. network
classifier, which provides better results compared to conventional
motion-based tracking algorithms. Network adaptation is accomplished
through an efficient and cost effective weight updating algorithm,
providing a minimum degradation of the previous network knowledge and
taking into account the current content conditions. A retraining set is
constructed and used for this purpose based on initial VO estimation
results. Two different scenarios are investigated. The first concerns
extraction of human entities in video conferencing applications, while
the second exploits depth information to identify generic VOs in
stereoscopic video sequences. Human face/body detection based on
Gaussian distributions is accomplished in the first scenario, while
segmentation fusion is obtained using color and depth information in
the second scenario. A decision mechanism is also incorporated to
detect time instances for weight updating. Experimental results and
comparisons indicate the good performance of the proposed scheme even
in sequences with complicated content (object bending, occlusion).
- Radke, RJ, Ramadge, PJ, Kulkarni, SR, and Echigo, T, "Efficiently synthesizing virtual video," IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 13, pp. 325-337, 2003.
Abstract:
Given a set of synchronized video sequences of a dynamic scene taken by
different cameras, we address the problem of creating a virtual video
of the scene from a novel viewpoint. A key aspect of our algorithm is a
method for recursively propagating dense and physically accurate
correspondences between the two video sources. By exploiting temporal
continuity and suitably constraining the correspondences, we provide an
efficient framework for synthesizing realistic virtual video. The
stability of the propagation algorithm is analyzed, and experimental
results are presented.
- Kimmel, R, and Bruckstein, AM, "Regularized Laplacian zero crossings as optimal edge integrators," INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 53, pp. 225-243, 2003.
Abstract:
We view the fundamental edge integration problem for object
segmentation in a geometric variational framework. First we show that
the classical zero-crossings of the image Laplacian edge detector as
suggested by Marr and Hildreth, inherently provides optimal
edge-integration with regard to a very natural geometric functional.
This functional accumulates the inner product between the normal to the
edge and the gray level image-gradient along the edge. We use this
observation to derive new and highly accurate active contours based on
this functional and regularized by previously proposed geodesic active
contour geometric variational models. We also incorporate a 2D
geometric variational explanation to the Haralick edge detector into
the geometric active contour framework.
- Bredno, J, Lehmann, TM, and Spitzer, K, "A general discrete contour model in two, three, and four dimensions for topology-adaptive multichannel segmentation," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 25, pp. 550-563, 2003.
Abstract:
We present a discrete contour model for the segmentation of image data
with any dimension of image domain and value range. The model consists
of a representation using simplex meshes and a mechanical formulation
of influences that drive an iterative segmentation. The object's
representation as well as the influences are valid for any dimension of
the image domain. The image influences introduced here, can combine
information from independent channels of higher-dimensional value
ranges. Additionally, the topology of the model automatically adapts to
objects contained in images. Noncontextual tests have validated the
ability of the model to reproducibly delineate synthetic objects. In
particular, images with a signal to noise ratio of SNR < 0.5 are
delineated within two pixels of their ground truth contour. Contextual
validations have shown the applicability of the model for medical image
analysis in image domains of two, three, and four dimensions in single
as well as multichannel value ranges.
- Ray, N, Acton, ST, Altes, T, de Lange, EE, and Brookeman, JR, "Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 22, pp. 189-199, 2003.
Abstract:
Inhaled hyperpolarized helium-3 (He-3) gas is a new magnetic resonance
(MR) contrast agent that is being used to study lung functionality. To
evaluate the total lung ventilation from the hyperpolarized He-3 MR
images, it is necessary to segment the lung cavities. This is difficult
to accomplish using only the hyperpolarized He-3 MR images, so
traditional proton (H-1) MR images are frequently obtained concurrent
with the hyperpolarized He-3 MR examination. Segmentation of the lung
cavities from traditional proton (H-1) MRI is a necessary first step in
the analysis of hyperpolarized He-3 MR images. In this paper, we
develop an active contour model that provides a smooth boundary and
accurately, captures the high curvature features of the lung cavities
from the H-3 MR images. This segmentation method is the first
parametric active contour model that facilitates straightforward
merging of multiple contours. The proposed method of merging computes
an external force field that is based on the solution of partial
differential equations with boundary condition defined by the initial
positions of the evolving contours. A theoretical connection with fluid
flow in porous media and the proposed force field is established. Then
by using the properties of fluid flow we prove that the proposed method
indeed achieves merging and the contours stop at the object boundary as
well. Experimental results involving merging in synthetic images are
provided. The segmentation technique has been employed in lung H-1 MR
imaging for segmenting the total lung air space. This technology plays
a key role in computing the functional air space from MR images that
use hyperpolarized He-3 gas as a contrast agent.
- Lehmann, TM, Bredno, J, and Spitzer, K, "On the design of active contours for medical image segmentation - A scheme for classification and construction," METHODS OF INFORMATION IN MEDICINE, vol. 42, pp. 89-98, 2003.
Abstract:
Objectives: To provide a comprehensive bottom-up categorization of
model-based segmentation techniques that allows to select, implement,
and apply well-suited active contour models for segmentation of medical
images, where major challenges are the high variability in shape and
appearance of objects, noise, artifacts, partial occlusions of objects,
and the required reliability and correctness of results.
Methods: We consider the general purpose of segmentation, the dimension
of images, the object representation within the model image and contour
influences, as well as the solution and the parameter selection of the
model. Potentials and limits are characterized for all instances in
each category providing essential information for the application of
active contours to various purposes in medical image processing. Based
on prolaps surgery planning, we exemplify the use of the scheme to
successfully design robust 3D-segmentation.
Results: The construction scheme allows to design robust segmentation
methods, which, in particular, should avoid any gaps of dimension. Such
gaps result from different image domains and value ranges with respect
to the applied model domain and the dimension of relevant subsets for
image influences, respectively.
Conclusions: A general segmentation procedure with sufficient
robustness for medical applications is still missing. IT is shown that
in almost every category, novel techniques are available to improve the
initial snake model, which was introduced in 1987.
- Reed, S, Petillot, Y, and Bell, J, "An automatic approach to the detection and extraction of mine features in sidescan sonar," IEEE JOURNAL OF OCEANIC ENGINEERING, vol. 28, pp. 90-105, 2003.
Abstract:
Mine detection and classification using high-resolution sidescan sonar
is a critical technology for mine counter measures (MCM). As opposed to
the majority of techniques which require large training data sets, this
paper presents unsupervised models for both the detection and the
shadow extraction phases of an automated classification system. The
detection phase is carried out using an unsupervised Markov random
field (MRF) model where the required model parameters are estimated
from the original image. Using a priori spatial information on the
physical size and geometric signature. of mines in sidescan sonar, a
detection-orientated MRF model is developed which directly segments the
image into regions of shadow, seabottom-reverberation, and
object-highlight. After detection, features are extracted so that the
object can be classified. A novel co-operating statistical snake (CSS)
model is presented which extracts the highlight and shadow of the
object. The CSS model again utilizes available a priori information on
the spatial relationship between the highlight and shadow, allowing
accurate segmentation of the object's shadow to be achieved on a wide
range of seabed types. Results are given for both models on real and
synthetic images and are shown to compare favorably with other models
in this field.
- Santarelli, MF, Positano, V, Michelassi, C, Lombardi, M, and Landini, L, "Automated cardiac MR image segmentation: theory and measurement evaluation," MEDICAL ENGINEERING & PHYSICS, vol. 25, pp. 149-159, 2003.
Abstract:
We present a new approach to magnetic resonance image segmentation with
a Gradient-Vector-Flow-based snake applied to selective smoothing
filtered images. The system also allows automated image segmentation in
the presence of grey scale inhomogeneity, as in cardiac Magnetic
Resonance imaging. Removal of such inhomogeneities is a difficult task,
but we proved that using non-linear anisotropic diffusion filtering,
myocardium edges are selectively preserved. The approach allowed
medical data to be automatically segmented in order to track not only
endocardium, which is usually a less difficult task, but also
epicardium in anatomic and perfusion studies with Magnetic Resonance.
The method developed proceeds in three distinct phases: (a) an
anisotropic diffusion filtering tool is used to reduce grey scale
inhomogeneity and to selectively preserve edges; (b) a
Gradient-Vector-Flow-based snake is applied on filtered images to allow
capturing a snake from a long range and to move into concave boundary
regions; and (c) an automatic procedure based on a snake is used to fit
both endocardium and epicardium borders in a multiphase, multislice
examination. A good agreement (P < 0.001) between manual and automatic
data analysis, based on the mean difference+/-SD, was assessed in a
pool of 907 cardiac function and perfusion images. (C) 2002 IPEM.
Published by Elsevier Science Ltd. All rights reserved.
- Zhang, ZX, and Braun, M, "Smoothness-based forces for deformable models: a long-range force and a corner fitting force," COMPUTERS IN BIOLOGY AND MEDICINE, vol. 33, pp. 91-112, 2003.
Abstract:
Deformable models, originally proposed by Terzopoulos et al. (Artif.
Intell. 36 (1988) 91) and Kass et al. (Int. J. Comput. Vision 1 (1988)
321) in 1988, have been widely used in medical image segmentation.
However, they manifest two well-known limitations: the lack of an
appropriate long-range force to drive the model surface towards the
object boundary and poor performance at high curvature boundaries (such
as corners) due to the models' intrinsic smoothness constraint. In this
paper, a new smoothness force with local control is proposed. The local
control is used to devise a long-range force, referred to as the
self-zoom force, and a corner fitting force. The self-zoom force
enables the model surface to expand and shrink without a limit in
range. The corner fitting force propels the model surface to fit
high-curvature boundaries. Experiments demonstrate that the model
surface is driven to the object boundary by the new forces even if the
initial estimate is not close and the object is nonconvex or has a high
local curvature. (C) 2002 Elsevier Science Ltd. All rights reserved.
- Guevara, MA, Silva, A, Oliveira, H, Pereira, MD, and Morgado, F, "Segmentation and morphometry of histological sections using deformable models: A new tool for evaluating testicular histopathology," PROGRESS IN PATTERN RECOGNITION, SPEECH AND IMAGE ANALYSIS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2905, pp. 282-290, 2003.
Abstract:
This paper presents a tool that uses image segmentation and
morphometric methods to evaluate testicular toxicity through the
analysis of histological sections of mice testis. The tool is based on
deformable models (Snakes) and includes several adaptations to solve
important difficulties of histological sections imaging, mainly the low
contrast edges between the boundary tissue of seminiferous tubules and
the interstitial tissue. The method is designed to produce accurate
segmentation and to keep track of tubular identities on images under
study. The extracted data can be used straightforwardly to compute
quantitative parameters characterizing tubular morphology. The method
was validated on a realistic data set and the results were compared
with those obtained with traditional techniques. The application of
this new technique facilitates measurements allowing assessing a higher
number of tubules in a fastest and accurate way.
- Kammerer, P, Langs, G, Sablatnig, R, and Zolda, E, "Stroke boundary analysis for identification of drawing tools," PROGRESS IN PATTERN RECOGNITION, SPEECH AND IMAGE ANALYSIS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 2905, pp. 408-415, 2003.
Abstract:
An algorithm for the automatic identification of drawing tools based on
the appearance of the stroke boundary is presented. The purpose of this
stroke analysis is the determination of drawing tools in underdrawings
- the basic concept of an artist - in ancient panel paintings. This
information allows significant support for a systematic stylistic
approach in the analysis of paintings. Up to now the identification of
drawing tools is performed by an expert visually. Our tool will support
the expert to investigate larger numbers of underdrawings, provides
objective and reproducible information and simplifies comparison of
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