Fuzzy based segmentation of multiple sclerosis lesions in Magnetic Resonance brain images

Segmentation is an important step for the diagnosis of multiple sclerosis. This paper presents a new approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. At first, Brain image is considered to be three parts, namely the dark, the gray, and the white part. Then, the fuzzy regions of their member functions are determined by maximizing fuzzy entropy through the Genetic algorithm. Finally, MS lesions and CSF areas are determined by applying a localized weighted filter to the Bright and Dark membership images. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with Gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.