feature selection in order to extract multiple sclerosis lesions automatically in 3d brain magnetic resonance images using combination of support vector machine and genetic algorithm

This paper presents a new feature selection approach for automatically extracting multiple sclerosis (ms) lesions in three?dimensional (3d) magnetic resonance (mr) images. presented method is applicable to different types of ms lesions. in this method,t1, t2, and fluid attenuated inversion recovery (flair) images are firstly preprocessed. in the next phase, effective features to extractms lesions are selected by using a genetic algorithm (ga). the fitness function of the ga is the similarity index (si) of a supportvector machine (svm) classifier. the results obtained on different types of lesions have been evaluated by comparison with manualsegmentations. this algorithm is evaluated on 15 real 3d mr images using several measures. as a result, the si between ms regionsdetermined by the proposed method and radiologists was 87% on average. experiments and comparisons with other methods showthe effectiveness and the efficiency of the proposed approach.