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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.


TPVF and FPVF errors for segmentation of GM.
© Copyright Policy - open-access
Related In: Results  -  Collection


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Figure 8: TPVF and FPVF errors for segmentation of GM.

Mentions: Accuracy of the object contours obtained with the proposedmultiphase level set deformable model and the three othersegmentation methods was evaluated by comparing segmentationresults to the manual ground truth. Bar plots of errormeasurements for the segmentation of the ten clinical casesand average errors are reported in Figures 8,9, and 10. Because TPVF and FNVF are bothrelative measures with respect to the ground truth volume, we have TPVF + FNVF = 1. Therefore, we only generated plotsfor TPVF and FPVF.


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

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

TPVF and FPVF errors for segmentation of GM.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: TPVF and FPVF errors for segmentation of GM.
Mentions: Accuracy of the object contours obtained with the proposedmultiphase level set deformable model and the three othersegmentation methods was evaluated by comparing segmentationresults to the manual ground truth. Bar plots of errormeasurements for the segmentation of the ten clinical casesand average errors are reported in Figures 8,9, and 10. Because TPVF and FNVF are bothrelative measures with respect to the ground truth volume, we have TPVF + FNVF = 1. Therefore, we only generated plotsfor TPVF and FPVF.

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.