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Brain MR Image Segmentation Technique : A Review

Brain image segmentation is one of the important parts of clinical diagnostic tools. Magnetic Resonance Images are used to produce images of soft tissue of human body. For detection of brain tumor, brain image segmentation is required, which is the process of partitioning the image into distinct regions, is one of the most important and challenging aspect of computer aided clinical diagnostic tools. Noises present in the Brain MRI images are multiplicative noises and reductions of these noises are difficult task. A large variety of algorithms for segmentation of MRI images had been developed. The review covers a classification of several segmentation algorithms ranging from simple threshold methods to high level segmentation approaches as deformable methods, classifier methods and graph based approaches. Region growing method is a fast, work quite well in finding high contrast boundary. Clustering methods are suitable to implement with a spatial constraint, also the review covers imaging modalities, magnetic resonance imaging and methods for noise reduction and segmentation approaches. The paper concludes with a discussion on the upcoming method of advanced researches in brain image segmentation.