Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts

A novel framework for joint clustering and point-by-point mapping of white matter fiber pathways is presented. Accurate clustering of the trajectories into fiber bundles requires point correspondence along the fiber pathways determined. This knowledge is also crucial for any tract-oriented quantitative analysis. We employ an expectationmaximization (EM) algorithm to cluster the trajectories in a Gamma mixture model context. The result of clustering is the probabilistic assignment of the fiber trajectories to each cluster, an estimate of the cluster parameters, and point correspondences. Point-by-point correspondence of the trajectories within a bundle is obtained by constructing a distance map and a label map from each cluster center at every iteration of the EM algorithm. This offers a time-efficient alternative to pairwise curve matching of all trajectories with respect to each cluster center. Probabilistic assignment of the trajectories to clusters is controlled by imposing a minimu...

• Type: Conference
• Year: 2007
• Where: IPMI
• Authors: Mahnaz Maddah, William M. Wells III, Simon K. Warfield, Carl-Fredrik Westin, W. Eric L. Grimson
• Keywords: Cluster Center, Fiber Trajectories, IPMI 2007, Matter Fiber Pathways, Medical Imaging