A hybrid method for brain MRI classification

We proposed an automatic hybrid image segmentation model that integrates the modified statistical expectation maximization (EM) method and the spatial information combined with Support Vector Machines (SVM). To improve the overall segmentation performance different types of information are integrated in this study, which are, voxel location, textural features, MR intensity and relationship with neighboring voxels. The modified EM method is used for intensity based classification as an initial segmentation stage. Secondly simple and beneficial features are extracted from target area of segmented image using gray-level co-occurrence matrix (GLCM) technique. Subsequently, we applied Support Vector Machine (SVM) to rank computed features from the extracted features, which is an enhancement step. To evaluate the performance of the proposed method, experiments carried out on real MRI. The results of proposed method are evaluated against manual segmentation results on real scans. The K-index is calculated to evaluate the performance of the proposed model relative to the expert segmentations. The results demonstrate that the proposed technique has satisfactory results.