Region of Interest Identification for Brain Tumors in Magnetic Resonance Images

Abstract

Glioma is a common type of brain tumor, and accurate detection of it plays a vital role in the diagnosis and treatment process. Despite advances in medical image analyzing, accurate tumor segmentation in brain magnetic resonance (MR) images remains a challenge due to variations in tumor texture, position, and shape. In this paper, we propose a fast, automated method, with light computational complexity, to find the smallest bounding box around the tumor region. This region-of-interest can be used as a preprocessing step in training networks for subregion tumor segmentation. By adopting the outputs of this algorithm, redundant information is removed; hence the network can focus on learning notable features related to subregions' classes. The proposed method has six main stages, in which the brain segmentation is the most vital step. Expectation-maximization (EM) and K-means algorithms are used for brain segmentation. The proposed method is evaluated on the BraTS 2015 dataset, and the average gained DICE score is 0.73, which is an acceptable result for this application.