A Review on Brain Tumor Segmentation and Definition using Soft Computing Technique

Ms.Pooja S. Deshpande, Mr. Shikant Honade
2017 IOSR Journal of Electronics and Communication Engineering  
Brain tumor is most severe disease; most of populations in world affected due to brain tumor. Now day death rate because brain tumor gradually increases. For that consideration, most prominent method implemented for brain tumor detection and segmentation. When most normal cells grow, old cells die or damaged and new cells take their place. Sometimes this process goes wrong. New cells form when the body does not need them, and old or damaged cell do not die as they should .The buildup of extras
more » ... ells often forms a mass of tissue called a growth or tumor. Earlier detection, diagnosis and proper treatment of brain tumor are essential to prevent human death. An effective brain tumor detection and segmentation using MR image is an essential task in medical field. A number of research papers related to medical image segmentation methods are studied. Segmentation and detection plays an important role in the processing of medical images. There are various segmentation methods implemented for brain tumor detection. These methods include k-means clustering with watershed segmentation algorithm; optimized k-means clustering with genetic algorithm and optimized c-means clustering with genetic algorithm. Traditional k-means algorithm is sensitive to the initial cluster centers. Genetic c-means and kmeans clustering techniques used to detect tumor in MRI of brain images etc. The proposed work deals with the use of soft computing technique for brain tumor detection and segmentation using MRI images. The soft computing technique gives more improved parameter.
doi:10.9790/2834-120203125128 fatcat:hsjhyc5c7veb3aldmqjpiwpmci