INTELLIGENT BRAIN TUMOR TISSUE SEGMENTATION FROM MAGNETIC RESONANCE IMAGE (MRI) USING META HEURISTIC ALGORITHMS

The Segmentation is a fundamental technique used in image processing to extract suspicious regions from the given image. In this paper proposes the meta-heuristic methods such as Ant Colony optimization (ACO), genetic algorithm (GA) and Particle swarm optimization (PSO) for segmenting brain tumors in 3D magnetic resonance images. Here this paper is divided into two stages. In the first stage preprocessing and enhancement is performed using tracking algorithms. These are used to preprocessing to suppress artifacts, remove unwanted skull portions from brain MRI and these images are enhanced using weighted median filter. The enhanced technique is evaluated by Peak Signal-to-Noise Ratio (PSNR) and Average Signal-to-Noise Ratio (ASNR) for filters. In the Second stage of the intelligent segmentation is three algorithms will be implemented for identifying and segmenting of suspicious region using ACO, GA and PSO, and their performance is studied. The proposed algorithms are tested with real patients MRI. Results obtained with a brain MRI indicate that this method can improve the sensitivity and reliability of the systems for automated detection of brain tumors .The algorithms are tested on 21 pairs of MRI from real patient’s brain database and evaluate the performance of the proposed method. Keywords- Brain Tumor, Magnetic Resonance Image (MRI), Preprocessing and Enhancement, Segmentation, Meta heuristic algorithm, Ant Colony optimization (ACO), genetic algorithm (GA) and Particle swarm optimization (PSO).