An Optimized Approach for Brain Tumor Segmentation Using Intelligent System

Abstract

ABSTRACT newline newline newline newline newlineThe death rate due to tumor has been increasing enormously over the past three decades. This fact increases the importance of research in the medical field, to identify brain pathologies for tumor segmentation and detection, which helps the neurosurgeon to diagnose brain disease and to set up most suitable treatment for the pathology. Normally, manual segmentation of brain image is a tedious and laborious task due to the processing of large amount of data and also due to the presence of minute brain lesions. Thus a completely automated segmentation system has become a real challenging in medical image processing, which has fascinated many researchers in this field, in recent years. The recommended system focus on all possible outcomes, that can be used to address the brain segmentation problems in multi modality MR images, which is widely used imaging technique for its high quality. Main idea of this proposed system is to consider this segmentation problem, with the aim to distinguish the normal and abnormal tissue pixel on the basis of texture feature. More precisely, the classifier used in this recommended system is Support Vector Machine (SVM), which is the most prevalent classification method used recently, along with a combination of Wrapper based Genetic Algorithm. newlineThe present work takes the advantage of pixel based information level, which uses T2 and Fluid Attenuated Inversion Recovery (FLAIR) multi modality image database. The recommended system mainly consists of two phases. Firstly, pre-processing followed by block division of the MR images. Secondly, feature extraction and selection and SVM classifier based training and testing. The feature extraction is done by a texture based method and feature selection by Genetic Algorithm and the selected features optimization is done by wrapper method. newlineThe performance of the proposed method has been tested using the brain MRI images from BRATS 2012 and DICOM Image Library data sets and real time data of Gokulam Medical Hospital, Kerala. In the p