Study of Various Methods for Brain Tumour Segmentation from MRI Images

Segmentation means segregating area of interest from the image. The aim of image segmentation is to cluster the pixels into salient image regions i.e. regions corresponding to individual surfaces, objects, or natural parts of objects. Automatic Brain tumour segmentation is a sensitive step in medical field. A significant medical informatics task is to perform the indexing of the patient databases according to image location, size and other characteristics of brain tumours based on magnetic resonance (MR) imagery. This requires segmenting tumours from different MR imaging modalities. Automated brain tumour segmentation from MR modalities is a challenging, computationally intensive task.Image segmentation plays an important role in image processing. MRI is generally more useful for brain tumour detection because it provides more detailed information about its type, position and size. For this reason, MRI imaging is the choice of study for the diagnostic purpose and, thereafter, for surgery and monitoring treatment outcomes. This paper presents a review of the various methods used in brain MRI image segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for segmentation approaches. The paper concludes with a discussion on the upcoming trend of advanced researches in brain image segmentation. Keywords-Region growing, Level set method, Split and merge algorithm, MRI images