Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

Robert Paul Velthuizen; Lawrence O. Hall; Laurence P. Clarke; Amine M. Bensaid; J. A. Arrington; Martin L. Silbiger

doi:10.1117/12.148675

29 July 1993 Unsupervised fuzzy segmentation of 3D magnetic resonance brain images

Robert Paul Velthuizen, Lawrence O. Hall, Laurence P. Clarke, Amine M. Bensaid, J. A. Arrington, Martin L. Silbiger

Proceedings Volume 1905, Biomedical Image Processing and Biomedical Visualization; (1993) https://doi.org/10.1117/12.148675
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

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.

Citation Download Citation

Robert Paul Velthuizen, Lawrence O. Hall, Laurence P. Clarke, Amine M. Bensaid, J. A. Arrington, and Martin L. Silbiger "Unsupervised fuzzy segmentation of 3D magnetic resonance brain images", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); https://doi.org/10.1117/12.148675

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