A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine

Abstract

In this paper, we present a new brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and cerebrospinal fluid (CSF) is extracted. These two steps are performed using the FAST toolbox (FMRIB's Automated Segmentation Tool) integrated in FSL software (FSL-FAST) developed in Oxford Centre for Functional MRI of the Brain (FMRIB). Then, in the third step, LS-SVM is used to segment grey matter (GM) and white matter (WM). The training samples for LS-SVM are selected from the registered brain atlas. Voxel intensities and spatial positions are selected as two feature groups for training and test. SVM as a powerful discriminator is able to handle nonlinear classification problems, however, it cannot provide posterior probability. Thus, we use a sigmoid function to map the SVM output into probabilities. The proposed method is used to segment CSF, GM and WM from simulated MR images using BrainWeb MR image simulator and real data provided by Internet Brain Segmentation Repository (IBSR). The automatically segmented tissues were evaluated by comparison with corresponding ground truth. The Dice and Jaccard similarity coefficients, sensitivity and specificity were calculated for quantitative validation of the results. The quantitative results show that the proposed method segments brain tissues accurately with respect to corresponding ground truth.