Comparative study of three methods for brain tumor detection and extraction using Image Segmentation Techniques

Abstract

Image segmentation is the process of dividing a digital image into image segments so that individual regions of interest can be analyzed and processed instead of the entire image. Image segmentation has a significant role in detecting regions of interest and extracting attributes and regions from those images. In this paper, five original grayscale abnormal MRI brain images have been processed by using image segmentation techniques for detecting and extracting regions of interest, in this case, tumors. This research described three methods of detection and extraction of tumors from abnormal MRI brain images in MATLAB: a method based on combined local threshold segmentation techniques with morphological operations for tumor detection; a method based on region splitting and merging segmentation techniques; and a method based on combined thresholding, Meyer's flooding watershed algorithm, as an image segmentation technique with morphological operations for tumor detection. Abnormal MRI brain images were preprocessed in order to obtain suitable results. Image data used in this research were obtained from Radiopedia, an educational radiology resource. The best method for detecting and extracting tumors has been determined by comparing the results of accuracy, sensitivity, F-measure, precision, MCC, dice, jaccard, and specificity. Based on the results of these measurements, it has been concluded and confirmed that the first and third methods are both equally good for detecting and extracting tumors.