Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A Review

Abstract

Context: Medical imaging technologies are an indispensable tool in medicine today developed to satisfy the significant demand for information on medical imaging by visualizing internal organs for clinical analysis. This enables the radiologists and clinicians to accurately understand the patient’s condition and makes medical practices easier, more effective for patients, and cheaper for the healthcare system. Objective: The current study aimed at presenting a comprehensive review on the recent classification and segmentation techniques of brain tumors in magnetic resonance image (MRI). Data Source: Google Scholar, ScienceDirect, Web of Knowledge, Springer, and manual search of reference lists from 1990 to 2018. Inclusion Criteria: The current study considered brain tumors since they are relatively less common and more important compared with other tumors due to their high morbidity rate. Results: Many automated brain tumors segmentation algorithms of magnetic resonance imaging (MRI) were reviewed and discussed including their advantages and limitations to provide a clear insight into these algorithms. The review concentratedon the state-of-art methods of segmentation of MRI brain tumors since they attracted a significant attention in the recent two decades resulting in many algorithms being developed for automated, semi-automated, and interactive segmentation of brain tumors. While there is a significant development of segmentation algorithms, they are rarely used clinically due to lack of interaction between developers and clinicians. Conclusions: Most studies did not consider grading of brain tumors and did not distinguish to which grade the brain tumor belonged. This enables the developers to understand how the margins of brain tumors appear in medical images. Limitations: The most important limitations that make brain tumors segmentation remaina challenging task are the variety of the shape and intensity of tumors in addition to the probability of inhomogeneity of tumorous tissue.