Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2007-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2007/10526 |
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| _version_ | 1832559434958635008 |
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| author | Elsa D. Angelini Ting Song Brett D. Mensh Andrew F. Laine |
| author_facet | Elsa D. Angelini Ting Song Brett D. Mensh Andrew F. Laine |
| author_sort | Elsa D. Angelini |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a5a379b867aa485584513687ec913348 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2007-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-a5a379b867aa485584513687ec9133482025-02-03T01:30:06ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962007-01-01200710.1155/2007/1052610526Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative StudyElsa D. Angelini0Ting Song1Brett D. Mensh2Andrew F. Laine3Ecole Nationale Supérieure des Télécommunications, Groupe des Ecoles des Télécommunications, CNRS UMR 5141, Paris 75013, FranceDepartment of Biomedical Engineering, School of Engineering and Applied Science, Columbia University, New York, NY 10027, USADepartment of Biological Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY 10032, USADepartment of Biomedical Engineering, School of Engineering and Applied Science, Columbia University, New York, NY 10027, USAThis 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.http://dx.doi.org/10.1155/2007/10526 |
| spellingShingle | Elsa D. Angelini Ting Song Brett D. Mensh Andrew F. Laine Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study International Journal of Biomedical Imaging |
| title | Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study |
| title_full | Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study |
| title_fullStr | Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study |
| title_full_unstemmed | Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study |
| title_short | Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study |
| title_sort | brain mri segmentation with multiphase minimal partitioning a comparative study |
| url | http://dx.doi.org/10.1155/2007/10526 |
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