Evaluation of fiber clustering methods for diffusion tensor imaging

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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.
Original languageEnglish
Title of host publicationProceedings IEEE Visualization 2005
EditorsC. Silva, E. Gröller, H. Rushmeier
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Print)0-7803-9462-3
Publication statusPublished - 2005
EventIEEE Visualization 2005 (VIS 2005), October 23-28, 2005, Minneapolis, MN, USA - Minneapolis, MN, United States
Duration: 23 Oct 200528 Oct 2005


ConferenceIEEE Visualization 2005 (VIS 2005), October 23-28, 2005, Minneapolis, MN, USA
Abbreviated titleVIS 2005
CountryUnited States
CityMinneapolis, MN

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