Image Segmentation is an important and challenging factor in the field of medical image processing. In the present days, for the human body anatomical study and for the treatment planning medical science very much depend on the medical imaging technology and medical images. Specifically for the human brain, MRI (Magnetic Resonance Imaging) widely prefers and using for the 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. Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. MRI is a medical technique, mainly used by the radiologist for visualization of internal structure of the human body without any surgery. MRI provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumour. Accurate segmentation of MRI image is important for the diagnosis of brain tumor by computer aided clinical tool. After appropriate segmentation of brain MR images, tumor is classified to malignant and benign, which is a difficult task due to complexity and variation in tumor tissue characteristics like its shape, size, gray level intensities and location. Taking in to account the aforesaid challenges, this study is focussed towards highlighting the MRI brain image segmentation techniques. However, this paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation.