Segmentation of brain MRI with statistical and 2D wavelet features by using neural networks

In this paper, an efficient technique is proposed for the precise segmentation of normal and pathological tissues in the MRI brain images. The proposed segmentation technique initially performs classification process by utilizing FFBNN. Dual FFBNN networks are used in the classification process. The inputs for these networks are the features that are extracted in two ways from the MRI brain images. Five features are extracted from the MRI images: they are two dynamic statistical features and three 2D wavelet decomposition features. In Segmentation, the normal tissues such as WM (White Matter), GM (Gray Matter) and CSF (Cerebrospinal Fluid) are segmented from the normal MRI images and pathological tissues such as Edema and Tumor are segmented from the abnormal images. The non-cortical tissues in the normal images are removed by the preprocessing stage. The implementation result shows the efficiency of proposed tissue segmentation technique in segmenting the tissues accurately from the MRI images. The performance of the segmentation technique is evaluated by performance measures such as accuracy, specificity and sensitivity. The performance of segmentation process is analyzed using a defined set of MRI brain.