1. Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation
Publication:
S. Roy, A. Carass, J. L. Prince, D. L. Pham
"Subject specific sparse dictionary learning for atlas based brain MRI segmentation",
Machine Learning in Medical Imaging, pp. 248-255, 2014.
S. Roy, Q. He, E. Sweeney, A. Carass, D. S. Reich, J. L. Prince, D. L. Pham
"Subject specific sparse dictionary learning for atlas based brain MRI segmentation",
IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 5, 1598-1609, 2015.
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2. PET Attenuation Correction using Synthetic CT from Ultrashort Echo-time MRI
Publication:
S. Roy, W. T. Wang, A. Carass, J. L. Prince, J. A. Butman, D. L. Pham
"PET Attenuation Correction Using
Synthetic CT from Ultrashort Echo-Time MR Imaging"
Journal of Nuclear Medicine, vol. 55, no. 12, 2071-2077, 2014.
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3. Magnetic Resonance Image Example Based Contrast Synthesis (MIMECS)
Abstract: The tissue contrast of a magnetic
resonance (MR) neuroimaging dataset has an impact on the performance of image
analysis tasks such as registration and segmentation. The type of pulse
sequence, its implementation, and the scanner type and calibration determine
the tissue contrast that one will observe, and these details are difficult to
control in large cross-sectional or longitudinal studies. It is also common
to encounter a dataset in which a desired tissue contrast is missing, i.e.,
never actually acquired, which may prevent certain image processing steps
from being carried out or their results applied to alternate data may be
inconsistent with the rest of the study. This paper introduces a sparse prior
based technique that uses image patches from an atlas to synthesize contrasts
not present or not intensity normalized in the original dataset. The proposed
image synthesis technique is demonstrated using two applications. First, it
is used to normalize the intensity of images acquired using the same pulse sequence
but with different parameters or on different scanners. Second, it is used to
synthesize images with a different tissue contrast than that which was
acquired. Unlike previous synthesis methods, the proposed method does not
require images to be acquired using a particular pulse sequence or set of
pulse sequences or to carry out an image registration procedure as part of
the process. The method is shown to yield more consistent segmentations and
to work in the presence of mild pathologies.
Publication: S. Roy, A. Carass, J. L. Prince, "A
compressed sensing approach for MR tissue contrast synthesis", IPMI, pp. 371-383,2011. (Best Poster Award )
S. Roy, A. Carass, J. L. Prince,
"Magnetic Resonance Image Example based Contrast Synthesis", IEEE Trans. Medical Imaging, vol. 32, no. 12, pp. 2348 - 2363, 2013.
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Example:
Suppose we want to synthesize MPRAGE contrast of an SPGR image using atlases.
We can certainly use deformable
registration to register the subject image to an atlas SPGR image, and
transfer the deformation to the MPRAGE scan of the atlas. However,
registration might fail due to pathologies (such as lesions) between the
subject and the atlas. Our method can overcome this issue.
There might be changes in the anatomy that a registration can not reproduce.
An example is shown below where the subject has much larger ventricles than
the atlas. Registration can not successfully recover the large change.
However, MIMECS is not dependent on such differences.
4. Fuzzy C Means with Variable Compactness
Abstract:
Fuzzy c-means (FCM) clustering has been
extensively studied and widely applied in the tissue classification of
biomedical images. Previous enhancements to FCM have accounted for intensity
shading, membership smoothness, and variable cluster sizes. In this paper, we
introduce a new parameter called "compactness" which captures
additional information of the underlying clusters. We then propose a new
classification algorithm, FCM with variable compactness (FCMVC), to classify
three major tissues in brain MRIs by incorporating the compactness terms into
a previously reported improvement to FCM. Experiments on both simulated
phantoms and real magnetic resonance brain images show that the new method
improves the repeatability of the tissue classification for the same subject
with different acquisition protocols.
Publication: S. Roy, H. Agarwal, A. Carass, Y.
Bai, D. L. Pham, J. L. Prince, "Fuzzy c-means with
variable compactness", ISBI, pp. 452-455, 2008.
Software:
Download
executable
Example: An
SPGR and MPRAGE acquisition of the same subject, when segmented with
different methods, give different results, in general. Using FCMVC, the
segmentations are made closer.
The cortical surfaces (CRUISE),
generated from the fuzzy segmentations, match more closely to a set of
manually picked landmarks.
5. A Rician Mixture Model Classification Algorithm
Abstract:.
Tissue classification algorithms
developed for magnetic resonance images commonly assume a Gaussian model on
the statistics of noise in the image. While this is approximately true for
voxels having large intensities, it is less true as the underlying intensity
becomes smaller. In this paper, the Gaussian model is replaced with a Rician
model, which is a better approximation to the observed signal. A new
classification algorithm based on a finite mixture model of Rician signals is
presented wherein the expectation maximization algorithm is used to find the
joint maximum likelihood estimates of the unknown mixture parameters.
Improved accuracy of tissue classification is demonstrated on several sample
data sets. It is also shown that classification repeatability for the same
subject under different MR acquisitions is improved using the new method.
Publication:
S. Roy, A. Carass, P. L. Bazin, S. Resnick, J. L. Prince, "Consistent
segmentation using a Rician classifier",
Medical Image Analysis, vol. 16, no. 2, pp. 524-535, 2011.
Software:
Download
executable
Example: An
SPGR and MPRAGE acquisition of the same subject, in general, have different
intensity histograms. As the noise in the MR intensities are primarily of Rician nature,
a mixture of Ricians fit the histograms better than a mixture of Gaussians.
Better fit in intensity distributions is reflected in more consistent
segmentation between these two contrasts.