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Multiple sclerosis lesion detection using constrained GMM and curve evolution.

Freifeld O, Greenspan H, Goldberger J - Int J Biomed Imaging (2009)

Bottom Line: The proposed CGMM-CE algorithm is used to segment 3D MRI brain images with an arbitrary number of channels.The CGMM-CE algorithm is automated and does not require an atlas for initialization or parameter learning.Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv 69978, Israel. orenf@eng.tau.ac.il

ABSTRACT
This paper focuses on the detection and segmentation of Multiple Sclerosis (MS) lesions in magnetic resonance (MRI) brain images. To capture the complex tissue spatial layout, a probabilistic model termed Constrained Gaussian Mixture Model (CGMM) is proposed based on a mixture of multiple spatially oriented Gaussians per tissue. The intensity of a tissue is considered a global parameter and is constrained, by a parameter-tying scheme, to be the same value for the entire set of Gaussians that are related to the same tissue. MS lesions are identified as outlier Gaussian components and are grouped to form a new class in addition to the healthy tissue classes. A probability-based curve evolution technique is used to refine the delineation of lesion boundaries. The proposed CGMM-CE algorithm is used to segment 3D MRI brain images with an arbitrary number of channels. The CGMM-CE algorithm is automated and does not require an atlas for initialization or parameter learning. Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data.

No MeSH data available.


Related in: MedlinePlus

Intensity K-means segmentation. K = 3. BrainWeb data, slice 95 with 9% noise. Blue: CSF; Green: GM; Yellow: WM.
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Related In: Results  -  Collection


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fig5: Intensity K-means segmentation. K = 3. BrainWeb data, slice 95 with 9% noise. Blue: CSF; Green: GM; Yellow: WM.

Mentions: As preprocessing steps, the brain region was extracted and the intensity distribution of each channel was normalized to have zero mean and unit variance. Then, a K-means intensity-based clustering was performed with K = 3, to achieve an initial crude segmentation (see Figure 5). The global intensity parameters of each tissue were initialized as the sample mean and sample covariance of the extracted tissue segment.


Multiple sclerosis lesion detection using constrained GMM and curve evolution.

Freifeld O, Greenspan H, Goldberger J - Int J Biomed Imaging (2009)

Intensity K-means segmentation. K = 3. BrainWeb data, slice 95 with 9% noise. Blue: CSF; Green: GM; Yellow: WM.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2742654&req=5

fig5: Intensity K-means segmentation. K = 3. BrainWeb data, slice 95 with 9% noise. Blue: CSF; Green: GM; Yellow: WM.
Mentions: As preprocessing steps, the brain region was extracted and the intensity distribution of each channel was normalized to have zero mean and unit variance. Then, a K-means intensity-based clustering was performed with K = 3, to achieve an initial crude segmentation (see Figure 5). The global intensity parameters of each tissue were initialized as the sample mean and sample covariance of the extracted tissue segment.

Bottom Line: The proposed CGMM-CE algorithm is used to segment 3D MRI brain images with an arbitrary number of channels.The CGMM-CE algorithm is automated and does not require an atlas for initialization or parameter learning.Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv 69978, Israel. orenf@eng.tau.ac.il

ABSTRACT
This paper focuses on the detection and segmentation of Multiple Sclerosis (MS) lesions in magnetic resonance (MRI) brain images. To capture the complex tissue spatial layout, a probabilistic model termed Constrained Gaussian Mixture Model (CGMM) is proposed based on a mixture of multiple spatially oriented Gaussians per tissue. The intensity of a tissue is considered a global parameter and is constrained, by a parameter-tying scheme, to be the same value for the entire set of Gaussians that are related to the same tissue. MS lesions are identified as outlier Gaussian components and are grouped to form a new class in addition to the healthy tissue classes. A probability-based curve evolution technique is used to refine the delineation of lesion boundaries. The proposed CGMM-CE algorithm is used to segment 3D MRI brain images with an arbitrary number of channels. The CGMM-CE algorithm is automated and does not require an atlas for initialization or parameter learning. Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data.

No MeSH data available.


Related in: MedlinePlus