Unsupervised fuzzy segmentation of 3D magnetic resonance brain images
Robert P. Velthuizen, Lawrence Hall, Laurence P. Clarke, Amine M. Bensaid, J A Arrington, Martin L. Silbiger · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1993
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.