In today’s world, computer aided technology has touched every sphere of human life ranging from communication, smart systems and even medical diagnosis. One of the broadest and upcoming areas of research is biomedical image processing that includes biomedical signal gathering, picture processing, image forming, and display to medical diagnosis based on various features extracted from the images. One of the most challenging and complex areas of research in biomedical image processing is the segmentation and analysis of a brain tumor. The brain is the most complicated and delicate anatomical structure in human body. Statistics proves that, among various brain ailments, a brain tumor is the most fatal and in many cases those tumors become carcinogenic i.e., brain cancer. A brain tumor is characterized by an abnormal and uncontrolled growth of brain cells, and takes up space within the cranial cavity. It varies in shape, size, position and characteristics viz, can be benign or malignant, or even spread to different parts of brain and body, which makes the detection of a brain tumor very critical and challenging. The most vital information a neurologist or neurosurgeon needs to have is the precise size and location of the tumor in the brain and also whether it is causing any swelling or compression of the brain that may need urgent attention. Moreover, this information is also necessary for planning surgery or post-operative care that may include radiology. Imaging plays a critical role in detection of brain tumor. Magnetic Resonance Imaging (MRI) is important to provide detailed and very precise information about tumor size, location and compression of adjacent brain structures. MR Images are of very high resolution that can be analyzed using Computer aided tool for automatic segmentation and analysis of tumor. Computer aided systems are preferred over conventional manual segmentation because automated segmentation is highly accurate and precise, free from human error, and much faster than manual segmentation. So, there is a lot of research on the design of efficient algorithms for segmentation and analysis of a brain MR Image. To give a complete analysis of a brain MR Image segmentation, the chapter primarily throws light on four aspects, namely, Brief discussion about different brain imaging modalities. Comprehensive review of different existing segmentation techniques and their analysis. Detailed discussion on the two most modern brain segmentation techniques, one in spatial domain and the other in frequency domain using wavelet. A detailed comparative study of the different techniques discussed. This chapter aims at providing a 360 insight into the recent research areas of biomedical image processing on MR Images that involves an understanding of the physics of various imaging modalities, various technologies of segmentation, and also the latest hybrid procedures that involves different aspect of signal processing with spatial image operation. © 2018 by Taylor & Francis Group, LLC.