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Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation
Author(s) -
Barra Vincent,
Boire JeanYves
Publication year - 2000
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/(sici)1522-2586(200003)11:3<267::aid-jmri5>3.0.co;2-8
Subject(s) - voxel , pattern recognition (psychology) , artificial intelligence , cluster analysis , segmentation , wavelet , computer science , image segmentation , fuzzy logic , imaging phantom , representation (politics) , fuzzy clustering , mathematics , nuclear medicine , medicine , politics , political science , law
An algorithm for the segmentation of a single sequence of three‐dimensional magnetic resonance (MR) images into cerebrospinal fluid, gray matter, and white matter classes is proposed. This new method is a possibilistic clustering algorithm using the fuzzy theory as frame and the wavelet coefficients of the voxels as features to be clustered. Fuzzy logic models the uncertainty and imprecision inherent in MR images of the brain, while the wavelet representation allows for both spatial and textural information. The procedure is fast, unsupervised, and totally independent of any statistical assumptions. The method is tested on a phantom image, then applied to normal and Alzheimer's brains, and finally compared with another classic brain tissue segmentation method, affording a relevant classification of voxels into the different tissue classes. J. Magn. Reson. Imaging 2000;11:267–278. © 2000 Wiley‐Liss, Inc.