Brain Image Classification Using Learning Machine Approach and Brain Structure Analysis

Abstract

Abstract Computer Aided Diagnosis (CAD) for functional brain images helps to analysis the structure of the brain images. The Multi Level Discrete Wavelet Transform method helps to decompose image and then the features are extracted. The brain image is classified by using PNN-RBF training and classification method, whether the disease is of normal, benign or malignant stages. This method helps to detect the disease in early stage itself. The Fisher Discriminant Ratio (FDR) method is used to reduce the computational cost by identifying the relationships among discriminate brain areas. The Morphological Filter technique is used to segment the images according to the Region of Interest (ROI), which is applied to abnormal images. This method yields better accuracy and sensitivity to find the discriminate brain areas. The early diagnosis of the tumor in brain is possible.