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

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...

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Veröffentlicht in:	IEEE transactions on visualization and computer graphics Jg. 14; H. 5; S. 1044 - 1053
Hauptverfasser:	Zhang, Song, Correia, Stephen, Laidlaw, David H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
ISSN:	1077-2626, 1941-0506
Online-Zugang:	Volltext
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