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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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Related in: MedlinePlus

(a) Joint 2D intensity histogram of T1-W and T2-W MRI of the adult brain. The associated 1D histograms of each MRI modality are plotted on the left and top. Both individual histograms consist of three overlapped Gaussian distributions that approximate the expected tissue distribution of GM, WM, and CSF. (b) The scatter plot of the tissue intensities after applying tissue segmentation. The horizontal axis represents T1-W intensities and the vertical axis represents T2-W intensities. The red cloud corresponds to GM, the green to WM, and the blue to CSF.
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fig13: (a) Joint 2D intensity histogram of T1-W and T2-W MRI of the adult brain. The associated 1D histograms of each MRI modality are plotted on the left and top. Both individual histograms consist of three overlapped Gaussian distributions that approximate the expected tissue distribution of GM, WM, and CSF. (b) The scatter plot of the tissue intensities after applying tissue segmentation. The horizontal axis represents T1-W intensities and the vertical axis represents T2-W intensities. The red cloud corresponds to GM, the green to WM, and the blue to CSF.

Mentions: It can be noted from the 1D histogram of the bias-corrected T1-W MRI of an adult brain in Figure 8(a) that there is an overlap between different tissue classes. Also, it can be seen that an overlap between WM and GM tissue is higher than between GM and CSF. This overlap between the class distributions can cause ambiguities in the decision boundaries when intensity-based segmentation methods are used [21]. However, many researchers showed that adding additional MRI sequences with different contrast properties (e.g., T2-W MRI, Proton Density MRI) can improve intensity-based segmentation and help separate the class distributions [22–24]; see Figure 13.


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

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

(a) Joint 2D intensity histogram of T1-W and T2-W MRI of the adult brain. The associated 1D histograms of each MRI modality are plotted on the left and top. Both individual histograms consist of three overlapped Gaussian distributions that approximate the expected tissue distribution of GM, WM, and CSF. (b) The scatter plot of the tissue intensities after applying tissue segmentation. The horizontal axis represents T1-W intensities and the vertical axis represents T2-W intensities. The red cloud corresponds to GM, the green to WM, and the blue to CSF.
© Copyright Policy
Related In: Results  -  Collection

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

fig13: (a) Joint 2D intensity histogram of T1-W and T2-W MRI of the adult brain. The associated 1D histograms of each MRI modality are plotted on the left and top. Both individual histograms consist of three overlapped Gaussian distributions that approximate the expected tissue distribution of GM, WM, and CSF. (b) The scatter plot of the tissue intensities after applying tissue segmentation. The horizontal axis represents T1-W intensities and the vertical axis represents T2-W intensities. The red cloud corresponds to GM, the green to WM, and the blue to CSF.
Mentions: It can be noted from the 1D histogram of the bias-corrected T1-W MRI of an adult brain in Figure 8(a) that there is an overlap between different tissue classes. Also, it can be seen that an overlap between WM and GM tissue is higher than between GM and CSF. This overlap between the class distributions can cause ambiguities in the decision boundaries when intensity-based segmentation methods are used [21]. However, many researchers showed that adding additional MRI sequences with different contrast properties (e.g., T2-W MRI, Proton Density MRI) can improve intensity-based segmentation and help separate the class distributions [22–24]; see Figure 13.

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
Related in: MedlinePlus