An Effective Brain Tumor Detection and Segmentation Technique by Using Watershed Algorithm and Optimized Neural Network

Abstract

In recent years brain tumor detection using MRI images is an effective clinical research area since MRI images does not make any tissue damage with its radiation and provides useful information about the tissue we are using MRI images in our proposed work for the brain tumor detection. Our proposed brain tumor detection method consists of four sections, i.e., pre-processing, segmentation, feature extraction and classification. Initially the input image fetched from the MRI database will be subjected to skull stripping in order to remove the unwanted region from the image. Then the skull stripped image is segmented using efficient watershed segmentation algorithm. Afterwards from the segmented image shape, intensity and texture features will be extracted. Then that extracted features is given as the input to the ANN classifier. Here the ANN classifier is optimized by well-known ABC optimization technique in order to get the enhanced classification accuracy. Thus from the classified abnormal images the tumor and edema region will be separated using modified region growing algorithm. The results will be analyzed to demonstrate the performance of the proposed segmentation and classification technique with other existing techniques.