CAD Scheme Based Brain Lesion Segmentation and Classification Approach

Abstract

Segmentation is a key process in most imaging and classification analysis for Computer-Aided Diagnostic or radiological evaluation (CAD). The pixel based method is a key technique in k-means clustering, as this method is simple and computational complexity is low compared to other region-based or border-based methods. In addition, segmentation of biomedical images using the clustering concept as the number of clusters is known from images of particular regions of human anatomy. The K-means clustering technique is used to track tumor objects in Magnetic Resonance Imaging (MRI). The key concept of the segmentation algorithm is to convert an MR input image into a gradient image and then separate the tumor location in the MR image through the K-media pool. These methods can obtain segmentation of brain images to detect the size and region of the lesion. Therefore, the average k cluster can obtain a robust, effective and accurate segmentation of brain lesions in MRI images automatically and the run time for segmentation of a single lesion is 0.021106. The detection of the tumor and the removal of the magnetic resonance of the brain are performed using the MATLAB software. The automatic instrument is designed to quantify brain tumors using magnetic resonance sets is the main focus of the work. The different methods used for this concept in the content-based recovery system are precision, memory and precision value for visual words, descriptive color and border descriptors, diffused histogram of color and structure. It is expected that the experimental results of the proposed system will produce better results than other existing systems. Total accuracy of 95.6% is obtained using GLCM functions in MATLAB software.