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Radiologists use medical images to diagnose diseases precisely. However, identification of brain tumor from medical images is still a critical and complicated job for a radiologist. Brain tumor identification form magnetic resonance imaging (MRI) consists of several stages. Segmentation is known to be an essential step in medical imaging classification and analysis. Performing the brain MR images segmentation manually is a difficult task as there are several challenges associated with it. Radiologist and medical experts spend plenty of time for manually segmenting brain MR images, and this is a non- repeatable task. In view of this, an automatic segmentation of brain MR images is needed to correctly segment White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF) tissues of brain in a shorter span of time. The accurate segmentation is crucial as otherwise the wrong identification of disease can lead to severe consequences. Taking into account the aforesaid challenges, this research is focused towards highlighting the strengths and limitations of the earlier proposed segmentation techniques discussed in the contemporary literature. Besides summarizing the literature, the paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. However, articulating a new technique is beyond the scope of this paper.

A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images