Image segmentation is very important in computer vision for image recovery, visual summary, image base modeling, and for many other purposes. Despite many years of research and substantial contributions, image segmentation is still a very challenging task to suit for range of applications. Brain Magnetic Resonance Image (MRI) segmentation is one of the most challenging and time consuming task in the field of medical imaging. But by nature medical images are complex and noisy. This leads to the necessity of processes that reduces difficulties in analysis and improves quality of output. Even though several methods and encouraging results are obtained in medical imaging area, reproducible segmentation and grouping of abnormalities are still a thought provoking task due to the different shapes, locations and image intensities of different types of tumors. This paper critically reviews recent brain MRI segmentation methods along with their detailed analysis, and evaluation on the basis of various parameters. The study and evaluation is useful in improving the performance of existing methods as well as helpful in the development of new methods.

Image Segmentation, Brain MRI, Graph Cuts

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