Enhanced Brain Tumor Segmentation and Size Estimation in MRI Samples using Hybrid Optimization

Abstract

The area of medical imaging specialization, specifically in the context of brain tumor segmentation, has long been challenged by the inherent complexity and variability of brain structures. Traditional segmentation methods often struggle to accurately differentiate between the diverse types of tissues within the brain, such as white matter, grey matter, and cerebrospinal fluid, leading to suboptimal results in tumor identification and delineation. These limitations necessitate the development of more advanced and precise segmentation techniques to enhance diagnostic accuracy and treatment planning. In response to these challenges, the proposed study introduces a novel segmentation approach that combines the Grey Wolf Optimization approach and the Cuckoo Search approach within a Fuzzy C-Means (FCM) framework. The integration of GWO and CS is designed to leverage their respective strengths in optimizing the segmentation of brain tissues. This hybrid approach was rigorously tested across multiple Magnetic Resonance Imaging (MRI) datasets, demonstrating significant enhancements over existing segmentation methods. The study observed a 4,9 % improvement in accuracy, 3,5 % increase in precision, 4,5 % higher recall, 3,2 % less delay, and 2,5 % better specificity in tumor segmentation. The implications of these advancements are profound. By achieving higher precision and accuracy in brain tumor segmentation, the proposed method can substantially aid in early diagnosis and accurate staging of brain tumors, eventually leading to more effective treatment planning and improved patient outcomes. Furthermore, the integration of GWO and CS within the FCM process sets a new benchmark in medical imaging, paving the way for future investigation in the field of study