The advances in brain MR imaging have also provided large amount of data with an increasingly high level of quality. Enormous progress in accessing brain injury and exploring brain anatomy has been made using magnetic resonance imaging (MRI). Over the last few decades, the rapid development of noninvasive brain imaging technologies has opened new horizons in analysing and studying the brain anatomy and function. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. In this paper we review the most popular methods commonly used for brain MRI segmentation. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications.
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