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Brain MRI segmentation with multiphase minimal partitioning: a comparative study.

Angelini ED, Song T, Mensh BD, Laine AF - Int J Biomed Imaging (2007)

Bottom Line: Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method.Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions.A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.

View Article: PubMed Central - PubMed

Affiliation: Ecole Nationale Supérieure des Télécommunications, Groupe des Ecoles des Télécommunications, CNRS UMR 5141, 75013 Paris, France.

ABSTRACT
This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: "idealized" intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.

No MeSH data available.


Intensity distributions and statistics for (a, d) WM,(b, e) GM, and (c, f) CSF. (a–c) Average values on consecutiveslices within three MRI data sets represented with different linestyles for the three MRI cases. (d–f) Fit of the volume histogramsto Gaussian distributions for one MRI data set.
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Figure 4: Intensity distributions and statistics for (a, d) WM,(b, e) GM, and (c, f) CSF. (a–c) Average values on consecutiveslices within three MRI data sets represented with different linestyles for the three MRI cases. (d–f) Fit of the volume histogramsto Gaussian distributions for one MRI data set.

Mentions: Results, illustrated in Figure 4 for three typicalcases, showed very stable estimates of intensity mean and variance values for each tissue across the wholevolume, corroborating the accuracy of the homogeneity assumptionfor the three tissues, and the absence of strong bias-field inhomogeneity. Lower mean intensity values on extremity sliceswere computed on small tissue samples (less than 10 voxels)corresponding to small anatomical structures with relatively highpartial volume effects.


Brain MRI segmentation with multiphase minimal partitioning: a comparative study.

Angelini ED, Song T, Mensh BD, Laine AF - Int J Biomed Imaging (2007)

Intensity distributions and statistics for (a, d) WM,(b, e) GM, and (c, f) CSF. (a–c) Average values on consecutiveslices within three MRI data sets represented with different linestyles for the three MRI cases. (d–f) Fit of the volume histogramsto Gaussian distributions for one MRI data set.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2211521&req=5

Figure 4: Intensity distributions and statistics for (a, d) WM,(b, e) GM, and (c, f) CSF. (a–c) Average values on consecutiveslices within three MRI data sets represented with different linestyles for the three MRI cases. (d–f) Fit of the volume histogramsto Gaussian distributions for one MRI data set.
Mentions: Results, illustrated in Figure 4 for three typicalcases, showed very stable estimates of intensity mean and variance values for each tissue across the wholevolume, corroborating the accuracy of the homogeneity assumptionfor the three tissues, and the absence of strong bias-field inhomogeneity. Lower mean intensity values on extremity sliceswere computed on small tissue samples (less than 10 voxels)corresponding to small anatomical structures with relatively highpartial volume effects.

Bottom Line: Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method.Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions.A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.

View Article: PubMed Central - PubMed

Affiliation: Ecole Nationale Supérieure des Télécommunications, Groupe des Ecoles des Télécommunications, CNRS UMR 5141, 75013 Paris, France.

ABSTRACT
This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for a priori information. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: "idealized" intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method.

No MeSH data available.