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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.


Partitioning of the image into four phases using twocurves (average intensity values are designed asc00, c10, c01, c11).
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Related In: Results  -  Collection


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Figure 1: Partitioning of the image into four phases using twocurves (average intensity values are designed asc00, c10, c01, c11).

Mentions: Segmentation of the data is performed via minimization of thefunctional F with respect to (C, c1, c2). This energyfunctional can be extended to the segmentation of multiplehomogeneous objects in the image by using several curves{c1, c2, … , ci}. In the case of two curves the followingenergy functional is used:(2)F(c1,c2,c00,c01,c10,c11)      =μ1 length (c1)+μ2 length (c2)          +υ1 area (inside c1)+υ2area (inside c2)          +λ1∫inside c1, inside c2/u0−c11/dΩ          +λ2∫inside c1, outside c2/u0−c10/dΩ          +λ3∫outside c1, inside c2/u0−c01/dΩ          +λ4∫outside c1, outside c2/u0−c00/dΩ.The set of parameters (λ1, λ2, λ3, λ4, μ1, ν1, μ2, ν2) takes real positive values. The two closed curves (c1, c2) split the domain Ω into four phases defined by theirrelative positions as illustrated inFigure 1. Inside these four phases, u0 has meanintensity values (c00, c01, c10, c11).


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

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

Partitioning of the image into four phases using twocurves (average intensity values are designed asc00, c10, c01, c11).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Partitioning of the image into four phases using twocurves (average intensity values are designed asc00, c10, c01, c11).
Mentions: Segmentation of the data is performed via minimization of thefunctional F with respect to (C, c1, c2). This energyfunctional can be extended to the segmentation of multiplehomogeneous objects in the image by using several curves{c1, c2, … , ci}. In the case of two curves the followingenergy functional is used:(2)F(c1,c2,c00,c01,c10,c11)      =μ1 length (c1)+μ2 length (c2)          +υ1 area (inside c1)+υ2area (inside c2)          +λ1∫inside c1, inside c2/u0−c11/dΩ          +λ2∫inside c1, outside c2/u0−c10/dΩ          +λ3∫outside c1, inside c2/u0−c01/dΩ          +λ4∫outside c1, outside c2/u0−c00/dΩ.The set of parameters (λ1, λ2, λ3, λ4, μ1, ν1, μ2, ν2) takes real positive values. The two closed curves (c1, c2) split the domain Ω into four phases defined by theirrelative positions as illustrated inFigure 1. Inside these four phases, u0 has meanintensity values (c00, c01, c10, c11).

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.