An efficient wavelet-based image fusion for brain tumor detection and segmentation over PET and MRI image

Abstract

A brain tumor is an abnormal growth of cells, reproducing themselves in an uncontrolled manner. In a medical diagnosis system, the accurate detection of location and size plays a very important role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are the most widely used techniques for diagnosis. The detection of a brain tumor in PET and MRI images is a challenging task due to its low sensitive boundary pixels. For accurate detection of brain tumors, in this paper, an efficient fusion-based brain tumor detection and segmentation is proposed. Here, at first, we fuse the input image using a discrete wavelet transform (DWT) and novel fusion rule. After the fusion process, the gray-level co-occurrence matrix (GLCM) features are extracted. Then, we categorize the brain images as normal and abnormal images using Optimal Deep Neural Network (ODNN). Here, the network weights of DNN are optimally selected using Spider Monkey Optimization (SMO) algorithm. After the classification process, the brain tumor region is extracted from abnormal brain images using the weighted k-means technique. The performance of the proposed methodology is analyzed in terms of sensitivity, specificity, and accuracy.