An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization
In this paper, we propose a partial differential equation structure that permits an active contour method to obtain intensity inhomogeneous image segmentation. We consider fitted model comprised of local and global energy functions dictated by the scaled p-Laplace term acting as a length regularizat...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8489868/ |
| _version_ | 1818854599328530432 |
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| author | Shafiullah Soomro Toufique Ahmed Soomro Kwang Nam Choi |
| author_facet | Shafiullah Soomro Toufique Ahmed Soomro Kwang Nam Choi |
| author_sort | Shafiullah Soomro |
| collection | DOAJ |
| description | In this paper, we propose a partial differential equation structure that permits an active contour method to obtain intensity inhomogeneous image segmentation. We consider fitted model comprised of local and global energy functions dictated by the scaled p-Laplace term acting as a length regularization term. A new local model is formulated by taking bias field into the local fitted model, which improves the performance of the proposed method relatively. The scaled p-Laplace equation exhibited as a regularized length term, which is utilized to reduce the impact of noise over level set minimization while guaranteeing the curve not to go through feeble boundaries. Inhomogeneities comprise of unwanted pixel variations called bias field, which change the consequences of the level set-based methods. Thereby, Gaussian distribution is used for the approximation of the bias field, and further bias field is used for bias correction likewise. Moreover, local model has been remodeled by integrating bias field inside their local information; similarly, global model is also established on the pretext of the local model. At last, we demonstrate the results on some complex images to show the strong and exact segmentation results that are conceivable with this new class of dynamic active contour model. We have also performed statistical analysis on mammogram images using accuracy, sensitivity, and Dice index metrics. Results show that the proposed method gets high accuracy, sensitivity, and Dice index values compared to the previous state-of-the-art methods. |
| first_indexed | 2024-12-19T07:55:16Z |
| format | Article |
| id | doaj.art-c466e97fde274b8389040ca16e5b6163 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:55:16Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c466e97fde274b8389040ca16e5b61632022-12-21T20:30:02ZengIEEEIEEE Access2169-35362018-01-016582725828310.1109/ACCESS.2018.28748128489868An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length RegularizationShafiullah Soomro0Toufique Ahmed Soomro1Kwang Nam Choi2https://orcid.org/0000-0002-7420-9216Quaid-e-Awam University of Engineering Science and Technology, Larkana, PakistanSchool of Computing and Mathematics, Charles Sturt University, Sydney, NSW, AustraliaDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaIn this paper, we propose a partial differential equation structure that permits an active contour method to obtain intensity inhomogeneous image segmentation. We consider fitted model comprised of local and global energy functions dictated by the scaled p-Laplace term acting as a length regularization term. A new local model is formulated by taking bias field into the local fitted model, which improves the performance of the proposed method relatively. The scaled p-Laplace equation exhibited as a regularized length term, which is utilized to reduce the impact of noise over level set minimization while guaranteeing the curve not to go through feeble boundaries. Inhomogeneities comprise of unwanted pixel variations called bias field, which change the consequences of the level set-based methods. Thereby, Gaussian distribution is used for the approximation of the bias field, and further bias field is used for bias correction likewise. Moreover, local model has been remodeled by integrating bias field inside their local information; similarly, global model is also established on the pretext of the local model. At last, we demonstrate the results on some complex images to show the strong and exact segmentation results that are conceivable with this new class of dynamic active contour model. We have also performed statistical analysis on mammogram images using accuracy, sensitivity, and Dice index metrics. Results show that the proposed method gets high accuracy, sensitivity, and Dice index values compared to the previous state-of-the-art methods.https://ieeexplore.ieee.org/document/8489868/Active contoursbias fieldlevel setp-Laplace |
| spellingShingle | Shafiullah Soomro Toufique Ahmed Soomro Kwang Nam Choi An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization IEEE Access Active contours bias field level set p-Laplace |
| title | An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization |
| title_full | An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization |
| title_fullStr | An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization |
| title_full_unstemmed | An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization |
| title_short | An Active Contour Model Based on Region Based Fitting Terms Driven by p-Laplace Length Regularization |
| title_sort | active contour model based on region based fitting terms driven by p laplace length regularization |
| topic | Active contours bias field level set p-Laplace |
| url | https://ieeexplore.ieee.org/document/8489868/ |
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