A Robust MR Image Segmentation Method for Enhanced Brain Tumor Detection

In medical image analysis, the segmentation of brain tumors is a crucial component in treatment, encompassing tasks such as tumor identification, patient follow-up, and computer-guided surgery. To enhance treatment outcomes and increase the survival rates of subjects, it is essential to leverage pertinent information provided by magnetic resonance imaging (MRI). MRI, as an advanced imaging technique, provides comprehensive and pertinent information, including details about the size, location, and shape of brain tumors. However, the detection of brain tumors has been a challenging task due to the complex features in their appearance and boundaries. The focus of this paper is on presenting an image segmentation technique for the detection of brain tumors. The proposed work is delineated into three phases. In the initial phase, we employ an optimization approach to segment brain tissue using Fuzzy Particle Swarm Optimization. The second phase utilizes a fuzzy approach to identify brain tumors through Fuzzy C-Means. The third phase integrates the results from the previous steps and incorporates a qualitative reasoning model based on Mamdani fuzzy logic is integrated with an optimized rule set for precise brain tumor diagnosis. The results obtained indicate that the proposed approach significantly outperforms existing techniques, achieving a sensitivity of 92%, a specificity of 97%, and an accuracy of 99.71%.