segmentation of multiple sclerosis in brain MR images using spatially constrained possibilistic Fuzzy C-Means classification

This paper introduces a novel methodology for the segmentation of brain multiple sclerosis (ms) lesions in magnetic resonance imaging (mri) volumes using a new clustering algorithm named spatially constrained possibilistic fuzzy c?means (scpfcm). scpfcm uses membership, typicality, and spatial information to cluster each voxel. the proposed method relies on an initial segmentation of ms lesions in t1?w and t2?w images by applying scpfcm algorithm, and the t1 image is then used as a mask and is compared with t2 image. the proposed method was applied to 10 clinical mri datasets. the results obtained on different types of lesions have been evaluated by comparison with manual segmentations.