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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.


Box plot of TI values, over the four segmentation methods(IT, M3DLS, FC, HMRF-EM) for the 10 clinical cases. for GM, WM andCSF.
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Related In: Results  -  Collection


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Figure 11: Box plot of TI values, over the four segmentation methods(IT, M3DLS, FC, HMRF-EM) for the 10 clinical cases. for GM, WM andCSF.

Mentions: The four segmentation methods were compared statistically using acharacteristic index of their performance. For this task, theTanimoto index (TI) was selected [37]. This index is aquantitative parameter used to evaluate the segmentation resultsand is defined as(14)TI=TPVF1+FPVF. Because TI populations do not follow a normal distribution, anonparametric analysis was performed for the four methodsover the 10 segmented cases, measuring the differences of the TIindexes. Small p values (below 0.05) indicate a significantstatistical difference between the methods [53].Distributions of the TI index over the 10 cases for each methodare plotted in Figure 11.


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

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

Box plot of TI values, over the four segmentation methods(IT, M3DLS, FC, HMRF-EM) for the 10 clinical cases. for GM, WM andCSF.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 11: Box plot of TI values, over the four segmentation methods(IT, M3DLS, FC, HMRF-EM) for the 10 clinical cases. for GM, WM andCSF.
Mentions: The four segmentation methods were compared statistically using acharacteristic index of their performance. For this task, theTanimoto index (TI) was selected [37]. This index is aquantitative parameter used to evaluate the segmentation resultsand is defined as(14)TI=TPVF1+FPVF. Because TI populations do not follow a normal distribution, anonparametric analysis was performed for the four methodsover the 10 segmented cases, measuring the differences of the TIindexes. Small p values (below 0.05) indicate a significantstatistical difference between the methods [53].Distributions of the TI index over the 10 cases for each methodare plotted in Figure 11.

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.