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.

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].

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 differ