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

Despotović I, Goossens B, Philips W - Comput Math Methods Med (2015)

Bottom Line: 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.We highlight differences between them and discuss their capabilities, advantages, and limitations.Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue.

View Article: PubMed Central - PubMed

Affiliation: Department of Telecommunications and Information Processing TELIN-IPI-iMinds, Ghent University, St-Pietersnieuwstraat 41, 9000 Ghent, Belgium.

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.

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Illustration of image elements in the MRI of the brain. An image pixel (i, j) is represented with the square in the 2D MRI slice and an image voxel (x, y, z) is represented as the cube in 3D space.
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fig1: Illustration of image elements in the MRI of the brain. An image pixel (i, j) is represented with the square in the 2D MRI slice and an image voxel (x, y, z) is represented as the cube in 3D space.

Mentions: An image can be defined as a function I(i, j) in 2D space or I(i, j, k) in 3D space, where i = 0,…, M − 1, j = 1,…, N − 1, and k = 0,…, D − 1 denote spatial coordinates. The values (or amplitudes) of the functions I(i, j) and I(i, j, k) are intensity values and are typically represented by a gray value {0,…, 255} in MRI of the brain; see Figure 1. Every image consists of a finite set of image elements called pixels in 2D space or voxels in 3D space. Each image element is uniquely specified by its intensity value and its coordinates (i, j) for pixels and (i, j, k) for voxels, where i is the image row number, j is the image column number, and k is the slice number in a volumetric stack; see Figure 2.


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

Despotović I, Goossens B, Philips W - Comput Math Methods Med (2015)

Illustration of image elements in the MRI of the brain. An image pixel (i, j) is represented with the square in the 2D MRI slice and an image voxel (x, y, z) is represented as the cube in 3D space.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402572&req=5

fig1: Illustration of image elements in the MRI of the brain. An image pixel (i, j) is represented with the square in the 2D MRI slice and an image voxel (x, y, z) is represented as the cube in 3D space.
Mentions: An image can be defined as a function I(i, j) in 2D space or I(i, j, k) in 3D space, where i = 0,…, M − 1, j = 1,…, N − 1, and k = 0,…, D − 1 denote spatial coordinates. The values (or amplitudes) of the functions I(i, j) and I(i, j, k) are intensity values and are typically represented by a gray value {0,…, 255} in MRI of the brain; see Figure 1. Every image consists of a finite set of image elements called pixels in 2D space or voxels in 3D space. Each image element is uniquely specified by its intensity value and its coordinates (i, j) for pixels and (i, j, k) for voxels, where i is the image row number, j is the image column number, and k is the slice number in a volumetric stack; see Figure 2.

Bottom Line: 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.We highlight differences between them and discuss their capabilities, advantages, and limitations.Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue.

View Article: PubMed Central - PubMed

Affiliation: Department of Telecommunications and Information Processing TELIN-IPI-iMinds, Ghent University, St-Pietersnieuwstraat 41, 9000 Ghent, Belgium.

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.

Show MeSH