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A.: Locally-constrained region-based methods for dw-mri segmentation
, 2007
"... In this paper, we describe a method for segmenting fiber bundles from diffusion-weighted magnetic resonance images using a locally-constrained region based approach. From a pre-computed optimal path, the algorithm propagates outward capturing only those voxels which are locally connected to the fibe ..."
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Cited by 6 (3 self)
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In this paper, we describe a method for segmenting fiber bundles from diffusion-weighted magnetic resonance images using a locally-constrained region based approach. From a pre-computed optimal path, the algorithm propagates outward capturing only those voxels which are locally connected to the fiber bundle. Rather than attempting to find large numbers of open curves or single fibers, which individually have questionable meaning, this method segments the full fiber bundle region. The strengths of this approach include its ease-of-use, computational speed, and applicability to a wide range of fiber bundles. In this work, we show results for segmenting the cingulum bundle. Finally, we explain how this approach and extensions thereto overcome a major problem that typical region-based flows experience when attempting to segment neural fiber bundles. 1.
Localized Statistics for DW-MRI Fiber Bundle Segmentation
"... We describe a method for segmenting neural fiber bundles in diffusion-weighted magnetic resonance images (DW-MRI). As these bundles traverse the brain to connect regions, their local orientation of diffusion changes drastically, hence a constant global model is inaccurate. We propose a method to com ..."
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Cited by 3 (3 self)
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We describe a method for segmenting neural fiber bundles in diffusion-weighted magnetic resonance images (DW-MRI). As these bundles traverse the brain to connect regions, their local orientation of diffusion changes drastically, hence a constant global model is inaccurate. We propose a method to compute localized statistics on orientation information and use it to drive a variational active contour segmentation that accurately models the non-homogeneous orientation information present along the bundle. Initialized from a single fiber path, the proposed method proceeds to capture the entire bundle. We demonstrate results using the technique to segment the cingulum bundle and describe several extensions making the technique applicable to a wide range of tissues.
Bayesian framework for white matter fiber similarity measure
- Proceedings of International Symposium on Biomedical Imaging
, 2009
"... We provide a Bayesian framework for measuring similarity in white matter fiber bundles based on Gaussian Processes. This framework does not rely on point-to-point correspondences, it takes into account a priori information about the fiber struc-ture working with three dimensional curves instead of p ..."
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Cited by 2 (0 self)
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We provide a Bayesian framework for measuring similarity in white matter fiber bundles based on Gaussian Processes. This framework does not rely on point-to-point correspondences, it takes into account a priori information about the fiber struc-ture working with three dimensional curves instead of point sequences. Moreover, it spans an inner product space among curves together with its induced metric. Thus, it provides an environment to perform statistics on curves. Finally, we show clustering results to illustrate the utility of this model.
Recovering cerebral white matter structures with Spectral Clustering of Diffusion MRI Data
, 2007
"... appor t de r ech er ch e ..."
