2020/02/26 by Fateme Mostafaie, Mostafaie, Fateme, Reihaneh Teimouri +7
Computer Science · Neuroscience · #Advanced Neural Network Applications #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.11509
openalex publication_date 2020/02/26 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Glioma is a common type of brain tumor, and accurate detection of it plays a\nvital role in the diagnosis and treatment process. Despite advances in medical\nimage analyzing, accurate tumor segmentation in brain magnetic resonance (MR)\nimages remains a challenge due to variations in tumor texture, position, and\nshape. In this paper, we propose a fast, automated method, with light\ncomputational complexity, to find the smallest bounding box around the tumor\nregion. This region-of-interest can be used as a preprocessing step in training\nnetworks for subregion tumor segmentation. By adopting the outputs of this\nalgorithm, redundant information is removed; hence the network can focus on\nlearning notable features related to subregions' classes. The proposed method\nhas six main stages, in which the brain segmentation is the most vital step.\nExpectation-maximization (EM) and K-means algorithms are used for brain\nsegmentation. The proposed method is evaluated on the BraTS 2015 dataset, and\nthe average gained DICE score is 0.73, which is an acceptable result for this\napplication.\n