Exploration of Bioinspired Algorithms in Multithreshold Image Segmentation for Medical Images

The brain is essential for human functioning, but it is vulnerable to multiple pathologies, including strokes, brain tumors, and other disorders that compromise its structure and function. Medical images obtained through techniques using ionizing radiation or magnetic resonance imaging (MRI) are fundamental for evaluating these conditions.In this context, brain image segmentation becomes a critical tool for accurately delineating anatomical and pathological structures, facilitating both diagnosis and therapeutic planning. Segmentation is carried out in an unsupervised manner using techniques such as Kapur entropy, Tsallis entropy, and the multilevel Otsu method, supported by metaheuristic optimization algorithms that aim to determine the most effective segmentation thresholds