A brain abnormality detection and tissue segmentation technique by using dual mode classifier

In the analysis of brain Magnetic Resonance Images (MRI), tissue classification is an important issue. Many works have been done to classify the brain tissues from the brain MRI. This paper presents a new technique to classify the brain MRI images and to perform tissue classification by using Dual Mode Classifier (DMC). Initially, the brain MRI images are obtained from the brain databases and features such as covariance and correlation are calculated from the input brain MRI images. These calculated features are given to Feed Forward Back Propagation Neural Network (FFBNN) to detect whether the given MRI brain image is normal or abnormal. After detection, the resultant image is subjected to the segmentation process with the use of Optimized Region Growing (ORGW) technique to accomplish efficient segmentation. Following that, by utilizing Local Binary Pattern (LBP), texture feature is computed from the segmented brain MRI images. Then this texture feature is given as the input to the DMC which has two branches. One branch classifies the normal tissues such as Grey Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CF) and the other branch classifies the abnormal tissues such as Tumor and Edema. The performance of our proposed technique is compared with other techniques such as Conventional Region Growing (RGW), and MRGW.