Segmentation of Brain Tumors using Meta Heuristic Algorithms

In medical field, Magnetic Resonance Image (MRI) is used to differentiate pathological tissues from normal tissues, especially for brain tumors. These days, millions of medical images have been produced routinely in medical care centers. Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Image analysis is still performed manually which is often a difficult and time consuming task. As a result, there is an increasing need for computerized image analysis to facilitate image based diagnosis. In Computer Aided Systems (CAS), the analyzed computer based output has been used as a second opinion for physicians and radiologists to analyze and diagnose the patient details in a faster manner as compared to manual process. Using the automated CAS, identification of different tissues and pathologies is clear, accurate and more certain. In this paper different metaheuristic methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bacteria Foraging Optimization Algorithm (BFOA), and Artificial Bee Colony Optimization (ABCO) are analyzed for segmenting brain tumors in 2D magnetic resonance images.