Image segmentation for MR brain tumor detection using machine learning: A review

Toufique A. Soomro, Lihong Zheng, Ahmed J. Afifi, Ahmed Ali, Shafiullah Soomro, Ming Yin, Junbin Gao

Research output: Contribution to journalReview articlepeer-review

60 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)70-90
Number of pages21
JournalIEEE Reviews in Biomedical Engineering
Early online date23 Jun 2022
Publication statusPublished - Feb 2023


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