Abstract
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. 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. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.
Highlights
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
In this paper we review the most popular methods commonly used for brain magnetic resonance imaging (MRI) segmentation
In the paper of Wang et al [103], a rather simple and effective scheme is used to deal with the partial volume effect (PVE) problem and is based on the anatomical observation that the misclassified white matter (WM) voxels are surrounded by the cerebrospinal fluid (CSF) and gray matter (GM) and that mislabeled CSF voxels are unconnected from the true WM volume
Summary
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. The advances in brain MR imaging have provided large amount of data with an increasingly high level of quality The analysis of these large and complex MRI datasets has become a tedious and complex task for clinicians, who have to manually extract important information. MRI segmentation is commonly used for measuring and visualizing different brain structures, for delineating lesions, for analysing brain development, and for imageguided interventions and surgical planning. This diversity of image processing applications has led to development of various segmentation techniques of different accuracy and degree of complexity. After reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation
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