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Medical image segmentation plays an important role in medical-imaging applications and they provide a large amount of functional and anatomical information, which improve and facilitate diagnosis and disease therapy planning. However, the existence of image artifacts, such as intensity inhomogeneity, noise and partial volume in magnetic resonance images (MRIs), can adversely affect the quantitative image analysis. There are different segmentation methods in the literature, which segment brain MRI into white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). However, there is not a common algorithm that can be used for all types of images. We present a critical appraisal of the current status of techniques for MRI segmentation. In this paper, commonly used segmentation algorithms are reviewed and summarized with an emphasis on their characteristics, advantages and disadvantages of these techniques. These are categorized into five different groups based on their workflows and segmentation principles. Different solutions are also proposed to compensate the existing problems in each algorithm. This paper also addresses the issue of quantitative evaluation of segmentation results.

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