Diagnosis of a Brain at an early stage has become an important topic of research in recent time. Detection of tumor at an early stage for primary treatment increases the patient’s survival rate. Processing of Magnetic resonance image (MRI) for an early tumor detection face the challenge of high processing overhead due to large volume of image input to the processing system. This result to large delay and decreases in system efficiency. Hence, the need of an enhanced detection system for accurate segmentation and representation for a faster and accurate processing has evolved in recent past. This paper outlines a brief review on the developments made in the area of MRI processing for an early diagnosis and detection of brain tumor for segmentation, representation and applying new machine learning (ML) methods in the decision making. The current trends in the automation of brain tumor detection, advantages, limitations and the future perspective of existing methods for computer aided diagnosis in brain tumor detection is outlined.