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MRI segmentation of the human brain: challenges, methods, and applications

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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.
Keywords
EXPECTATION-MAXIMIZATION ALGORITHM, IMAGE SEGMENTATION, AUTOMATIC SEGMENTATION, ACTIVE CONTOURS, LEVEL SET, TISSUE CLASSIFICATION, NONRIGID REGISTRATION, SPATIAL INFORMATION, NEONATAL BRAIN, VARYING STATISTICAL CLASSIFICATION

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Chicago
Despotovic, Ivana, Bart Goossens, and Wilfried Philips. 2015. “MRI Segmentation of the Human Brain: Challenges, Methods, and Applications.” Computational and Mathematical Methods in Medicine.
APA
Despotovic, I., Goossens, B., & Philips, W. (2015). MRI segmentation of the human brain: challenges, methods, and applications. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE.
Vancouver
1.
Despotovic I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE. Hindawi Publishing Corporation; 2015;
MLA
Despotovic, Ivana, Bart Goossens, and Wilfried Philips. “MRI Segmentation of the Human Brain: Challenges, Methods, and Applications.” COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2015): n. pag. Print.
@article{5835610,
  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{\textquoteright}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.},
  articleno    = {450341},
  author       = {Despotovic, Ivana and Goossens, Bart and Philips, Wilfried},
  journal      = {COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE},
  keyword      = {EXPECTATION-MAXIMIZATION ALGORITHM,IMAGE SEGMENTATION,AUTOMATIC SEGMENTATION,ACTIVE CONTOURS,LEVEL SET,TISSUE CLASSIFICATION,NONRIGID REGISTRATION,SPATIAL INFORMATION,NEONATAL BRAIN,VARYING STATISTICAL CLASSIFICATION},
  language     = {eng},
  pages        = {23},
  publisher    = {Hindawi Publishing Corporation},
  title        = {MRI segmentation of the human brain: challenges, methods, and applications},
  url          = {http://dx.doi.org/10.1155/2015/450341},
  year         = {2015},
}

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