Segmentation of brain tissues is very important in medical image analysis.Support Vector Machines(SVM) is considered a good candidate because of its good generalization performance

especially for dataset with small number of samples in high demensional feature space.This paper investigates the segmentation of magnetic resonance brain tissues image based on SVM.Experimental results show that the influence of kernel function and model parameters on the generalization performance of SVM is significant;SVM is suitably used as learning classifier of small sample size;To segment targets with blurry edges

intensity non-uniformity and discontinuity(such as medical images)

SVM approach is a good choice.