Detection of normal and abnormal tissues in MR images of the brain using an advanced multilevel thresholding technique and kernel SVM classifier

Segmentation of MR images is more important and is an essential process in resolving the human tissues, especially at the time of clinical analysis. Brain tissue is explicitly complex and it consists of three normal main tissues named White Matter (WM), Gray Matter (GM) and Cerebral Spinal Fluid (CSF) and abnormal tissues like tumor and edema. These normal and abnormal tissues can be detected using segmentation of the brain MR image and are very important for surgical planning and in diagnosing neurological diseases. This paper presents a novel approach for the detection of normal and abnormal tissues. Anisotropic diffusion filtering is used as the preprocessing step. The part of the algorithm used to detect the healthy tissues WM, GM, and CSF is based on multilevel thresholding that selects the best threshold values using Electro-magnetism-Like algorithm. A Region-based Active Contour is used to detect the tumor. A trained Support Vector Machine with different kernels which can be treated as KSVM is used for the classification of the tumor. In the classification process, features are extracted from the tumor images using the single level decomposition of 2-D Daubechies DWT. The high dimensioned feature vector is reduced by using Independent Component Analysis (ICA). The suggested method can identify the benign and malignant type of tumors. The pursuance of the suggested method is evaluated in terms of sensitivity, specificity, segmentation accuracy using ground truth images and results are compared with some of the existing methods. Experimental results demonstrate the high performance of the proposed method.