NDLI: Evaluation of fiber clustering methods for diffusion tensor imaging

Content Provider	IEEE Xplore Digital Library
Author	Moberts, B. Vilanova, A. van Wijk, J.J.
Copyright Year	2005
Description	Author affiliation: Dept. of Math. & Comput. Sci., Technische Univ. Eindhoven, Netherlands (Moberts, B.)
Abstract	Fiber tracking is a standard approach for the visualization of the results of diffusion tensor imaging (DTI). If fibers are reconstructed and visualized individually through the complete white matter, the display gets easily cluttered making it difficult to get insight in the data. Various clustering techniques have been proposed to automatically obtain bundles that should represent anatomical structures, but it is unclear which clustering methods and parameter settings give the best results. We propose a framework to validate clustering methods for white-matter fibers. Clusters are compared with a manual classification which is used as a ground truth. For the quantitative evaluation of the methods, we developed a new measure to assess the difference between the ground truth and the clusterings. The measure was validated and calibrated by presenting different clusterings to physicians and asking them for their judgement. We found that the values of our new measure for different clusterings match well with the opinions of physicians. Using this framework, we have evaluated different clustering algorithms, including shared nearest neighbor clustering, which has not been used before for this purpose. We found that the use of hierarchical clustering using single-link and a fiber similarity measure based on the mean distance between fibers gave the best results.
Starting Page	65
Ending Page	72
File Size	11251037
Page Count	8
File Format	PDF
ISBN	0780394623
DOI	10.1109/VISUAL.2005.1532779
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods Diffusion tensor imaging Data visualization Clustering algorithms Biomedical measurements Brain Nearest neighbor searches Image reconstruction Streaming media Mathematics
Content Type	Text
Resource Type	Article

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