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Proceedings Paper

Unsupervised fuzzy segmentation of 3D magnetic resonance brain images
Author(s): Robert Paul Velthuizen; Lawrence O. Hall; Laurence P. Clarke; Amine M. Bensaid; J. A. Arrington; Martin L. Silbiger
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Paper Abstract

Unsupervised fuzzy methods are proposed for segmentation of 3D Magnetic Resonance images of the brain. Fuzzy c-means (FCM) has shown promising results for segmentation of single slices. FCM has been investigated for volume segmentations, both by combining results of single slices and by segmenting the full volume. Different strategies and initializations have been tried. In particular, two approaches have been used: (1) a method by which, iteratively, the furthest sample is split off to form a new cluster center, and (2) the traditional FCM in which the membership grade matrix is initialized in some way. Results have been compared with volume segmentations by k-means and with two supervised methods, k-nearest neighbors and region growing. Results of individual segmentations are presented as well as comparisons on the application of the different methods to a number of tumor patient data sets.

Paper Details

Date Published: 29 July 1993
PDF: 9 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148675
Show Author Affiliations
Robert Paul Velthuizen, Univ. of South Florida and H. Lee Moffitt Cancer and Research Institute (United States)
Lawrence O. Hall, Univ. of South Florida and H. Lee Moffitt Cancer and Research Institute (United States)
Laurence P. Clarke, Univ. of South Florida and H. Lee Moffitt Cancer and Research Institute (United States)
Amine M. Bensaid, Univ. of South Florida and H. Lee Moffitt Cancer and Research Institute (United States)
J. A. Arrington, Univ. of South Florida and H. Lee Moffitt Cancer and Research Institute (United States)
Martin L. Silbiger, Univ. of South Florida and H. Lee Moffitt Cancer and Research Institute (United States)


Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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