A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans
Background and objectives: The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features’ intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficu...
| Główni autorzy: | , , |
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| Format: | Artykuł |
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Elsevier
2018
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| Hasła przedmiotowe: |
| _version_ | 1825721760209698816 |
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| author | Ibrahim, Rabha Waell Hasan, Ali M. Jalab, Hamid Abdullah |
| author_facet | Ibrahim, Rabha Waell Hasan, Ali M. Jalab, Hamid Abdullah |
| author_sort | Ibrahim, Rabha Waell |
| collection | UM |
| description | Background and objectives: The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features’ intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method. Method: In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan–Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative. Results: The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%. Conclusions: The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method. |
| first_indexed | 2024-03-06T05:55:15Z |
| format | Article |
| id | um.eprints-21829 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:55:15Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-218292019-08-06T06:43:00Z http://eprints.um.edu.my/21829/ A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans Ibrahim, Rabha Waell Hasan, Ali M. Jalab, Hamid Abdullah QA75 Electronic computers. Computer science R Medicine Background and objectives: The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features’ intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method. Method: In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan–Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative. Results: The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%. Conclusions: The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method. Elsevier 2018 Article PeerReviewed Ibrahim, Rabha Waell and Hasan, Ali M. and Jalab, Hamid Abdullah (2018) A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans. Computer Methods and Programs in Biomedicine, 163. pp. 21-28. ISSN 0169-2607, DOI https://doi.org/10.1016/j.cmpb.2018.05.031 <https://doi.org/10.1016/j.cmpb.2018.05.031>. https://doi.org/10.1016/j.cmpb.2018.05.031 doi:10.1016/j.cmpb.2018.05.031 |
| spellingShingle | QA75 Electronic computers. Computer science R Medicine Ibrahim, Rabha Waell Hasan, Ali M. Jalab, Hamid Abdullah A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans |
| title | A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans |
| title_full | A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans |
| title_fullStr | A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans |
| title_full_unstemmed | A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans |
| title_short | A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans |
| title_sort | new deformable model based on fractional wright energy function for tumor segmentation of volumetric brain mri scans |
| topic | QA75 Electronic computers. Computer science R Medicine |
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