Sheet-like white matter fiber tracts: representation, clustering, and quantitative analysis

We introduce an automated and probabilistic method for subject-specific segmentation of sheet-like fiber tracts. In addition to clustering of trajectories into anatomically meaningful bundles, the method provides statistics of diffusion measures by establishing point correspondences on the estimated...

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Bibliographic Details
Published in:Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 14; no. Pt 2; p. 191
Main Authors: Maddah, Mahnaz, Miller, James V, Sullivan, Edith V, Pfefferbaum, Adolf, Rohlfing, Torsten
Format: Journal Article
Language:English
Published: Germany 2011
Subjects:
Adult
Aged
Aging
Algorithms
Brain - pathology
Brain Mapping - methods
Cluster Analysis
Humans
Image Processing, Computer-Assisted - methods
Likelihood Functions
Models, Statistical
Nerve Fibers, Myelinated - pathology
Probability
Online Access:Get more information
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Summary:We introduce an automated and probabilistic method for subject-specific segmentation of sheet-like fiber tracts. In addition to clustering of trajectories into anatomically meaningful bundles, the method provides statistics of diffusion measures by establishing point correspondences on the estimated medial representation of each bundle. We also introduce a new approach for medial surface generation of sheet-like fiber bundles in order too initialize the proposed clustering algorithm. Applying the new method to a population study of brain aging on 24 subjects demonstrates the capabilities and strengths of the algorithm in identifying and visualizing spatial patterns of group differences.