An improved tumor segmentation algorithm from T2 and FLAIR multimodality MRI brain images by support vector machine and genetic algorithm

Abstract

This paper puts forth a framework of a medical image analysis system for brain tumor segmentation. Image segmentation helps to segregate objects right from the background, thus proving to be a powerful tool in medical image processing. This paper presents an improved segmentation algorithm rooted in support vector machine (SVM) and genetic algorithm. SVMs are the basis of a machine-learning technique that has been used for segmentation and classification of medical images. The database consists of two weighted images: T2 and FLAIR. The proposed system mainly consists of two stages. The first stage performs pre-processing the MRI image, followed by block division. The second stage includes feature extraction, feature selection, and finally, the SVM-based training and testing. The feature extraction is done by first-order histogram and co-occurrence matrix; GA using wrapper method is used to select optimum subset features. Experimental outcomes for 10 patients with over 250 MRIs are proof of the proficiency of our work. The performance of the proposed system is evaluated in terms of specificity, sensitivity, accuracy; time elapsed and figure of merit. This approach also permits the segmentation of image volumes based on training sets selected on a single slice.

Keywords

• segmentation
• support vector machine
• genetic algorithm
• k nearest neighbors
• wrapper method