Identifying White-Matter Fiber Bundles in DTI Data Using an Automated Proximity-Based Fiber-Clustering Method

• Published in: IEEE transactions on visualization and computer graphics Vol. 14; no. 5; pp. 1044 - 1053
• Main Authors: Zhang, Song, Correia, Stephen, Laidlaw, David H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Anatomy; Artificial Intelligence; Brain - cytology; Bundles; cluster; Cluster Analysis; Clustering; Clustering algorithms; Clustering methods; Clusters; Diffusion Magnetic Resonance Imaging - methods; Diffusion tensor imaging; DT-MRI; DTI; Female; Fibers; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Labeling; Magnetic resonance imaging; Male; Marking; Mathematical models; Middle Aged; Nerve Fibers, Myelinated - ultrastructure; Optical fiber testing; Pattern Recognition, Automated - methods; Proximity; Reproducibility of Results; Robustness; Sampling methods; Sensitivity and Specificity; Termination of employment; DTI; cluster; DT-MRI; clustering; Diffusion Tensor Imaging

We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. W...