An Efficient Brain Tumor MRI Segmentation and Classification Using GLCM Texture Features and Feed Forward Neural Networks
Brain tumor detection and segmentation in magnetic Resonance Images (MRI) is vital in medical diagnosis as it provides information related to anatomical structures as well as potential abnormal tissues obligatory to treatment planning and patient record.But regrettably it is a very decisive task. We have proposed a new method. It provides a competent and fast approach for diagnosis of the brain tumor. The Proposed system consists of manifold phases. First phase consists of Preprocessing and segmentation, the second phased consists of first order and second order GLCM (Gray level Co occurrence Matrix) based features extraction from segmented brain MR images. Third phase classify brain images into tumor and non tumors using Feed Forwarded Artificial neural network based classifier. After classification tumor region is extracted from those images which are classified as malignant using two stage segmentation process. Experiments have exposed that the method was more robust to initialization, faster and accurate.