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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.


MRI brain data. (a) Original slice with corticalstructures. (b) Original data with noncortical structures removed.(c) Manually labeled data on cortical structures. (d) Simplifiedmanually labeled data used for the “ground truth.”
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Related In: Results  -  Collection


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Figure 3: MRI brain data. (a) Original slice with corticalstructures. (b) Original data with noncortical structures removed.(c) Manually labeled data on cortical structures. (d) Simplifiedmanually labeled data used for the “ground truth.”

Mentions: MRI volumes were preprocessed to remove all noncortical braintissue by using the manually labeled data sets as binary masks.This preprocessing is illustrated in Figure 3. Todetermine the practicality of masking out subcortical gray matteron naïve images, we constructed from a library of labeled atlases two probabilistic atlases in which eachvoxel was assigned a likelihood of being made of cortical graymatter or subcortical gray matter. Less than 0.1% of the voxelsin the whole brain simultaneously had over 20% chance of beingmade of both gray matter and subcortical gray matter. Suchstatistical finding confirms that one can apply apopulation-based method for masking out subcortical gray matterfor the purpose of applying cortical segmentation methods withoutintroducing significant errors.


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

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

MRI brain data. (a) Original slice with corticalstructures. (b) Original data with noncortical structures removed.(c) Manually labeled data on cortical structures. (d) Simplifiedmanually labeled data used for the “ground truth.”
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: MRI brain data. (a) Original slice with corticalstructures. (b) Original data with noncortical structures removed.(c) Manually labeled data on cortical structures. (d) Simplifiedmanually labeled data used for the “ground truth.”
Mentions: MRI volumes were preprocessed to remove all noncortical braintissue by using the manually labeled data sets as binary masks.This preprocessing is illustrated in Figure 3. Todetermine the practicality of masking out subcortical gray matteron naïve images, we constructed from a library of labeled atlases two probabilistic atlases in which eachvoxel was assigned a likelihood of being made of cortical graymatter or subcortical gray matter. Less than 0.1% of the voxelsin the whole brain simultaneously had over 20% chance of beingmade of both gray matter and subcortical gray matter. Suchstatistical finding confirms that one can apply apopulation-based method for masking out subcortical gray matterfor the purpose of applying cortical segmentation methods withoutintroducing significant errors.

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.