Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review

• Published in: IEEE reviews in biomedical engineering Vol. 16; pp. 70 - 90
• Main Authors: Soomro, Toufique A., Zheng, Lihong, Afifi, Ahmed J., Ali, Ahmed, Soomro, Shafiullah, Yin, Ming, Gao, Junbin
• Format: Journal Article
• Language: English
• Published: United States IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Algorithms; Anomalies; Brain; Brain - pathology; Brain cancer; Brain images; Brain Neoplasms; Brain research; Brain tumors; Cancer; CNN models; Computed tomography; Deep learning; Diseases; Health services; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medical research; Medical treatment; MRI; Neuroimaging; segmentation; Tumors
• ISSN: 1937-3333; 1941-1189
• DOI: 10.1109/RBME.2022.3185292

Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.