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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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(a) The PDF for the Rician distribution. (b) The PDF for the Gaussian distribution.
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fig7: (a) The PDF for the Rician distribution. (b) The PDF for the Gaussian distribution.

Mentions: It has been shown that the noise in the magnitude images is governed by a Rician distribution, based on the assumption that the noise on the real and imaginary channels is Gaussian [19]. The probability density function for a Rician distribution is defined as(4)fRicex=xσ2exp⁡−x2+ν22σ2I0xνσ2,where x is the measured pixel/voxel intensity, ν is the image pixel/voxel intensity in the absence of noise, σ is the standard deviation of the Gaussian noise in the real and the imaginary images, and I0 is the zero-order modified Bessel function of the first kind. The Rician probability density function (PDF) is plotted in Figure 7(a) for several values of the signal-to-noise ratio (SNR), where the SNR is defined as ν/σ (the power ratio between the signal and the background noise).


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

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

(a) The PDF for the Rician distribution. (b) The PDF for the Gaussian distribution.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: (a) The PDF for the Rician distribution. (b) The PDF for the Gaussian distribution.
Mentions: It has been shown that the noise in the magnitude images is governed by a Rician distribution, based on the assumption that the noise on the real and imaginary channels is Gaussian [19]. The probability density function for a Rician distribution is defined as(4)fRicex=xσ2exp⁡−x2+ν22σ2I0xνσ2,where x is the measured pixel/voxel intensity, ν is the image pixel/voxel intensity in the absence of noise, σ is the standard deviation of the Gaussian noise in the real and the imaginary images, and I0 is the zero-order modified Bessel function of the first kind. The Rician probability density function (PDF) is plotted in Figure 7(a) for several values of the signal-to-noise ratio (SNR), where the SNR is defined as ν/σ (the power ratio between the signal and the background noise).

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