Shape Prior Embedded Level Set Model for Image Segmentation
This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimize...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/9014217 |
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| _version_ | 1832560810968219648 |
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| author | Wansuo Liu Dengwei Wang Wenjun Shi |
| author_facet | Wansuo Liu Dengwei Wang Wenjun Shi |
| author_sort | Wansuo Liu |
| collection | DOAJ |
| description | This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail. |
| format | Article |
| id | doaj-art-60127285b1c84bf4b9810bec796d68a7 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-60127285b1c84bf4b9810bec796d68a72025-02-03T01:26:36ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552019-01-01201910.1155/2019/90142179014217Shape Prior Embedded Level Set Model for Image SegmentationWansuo Liu0Dengwei Wang1Wenjun Shi2Aviation Maintenance School for NCO, Air Force Engineering University, Xinyang 464000, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaAviation Maintenance School for NCO, Air Force Engineering University, Xinyang 464000, ChinaThis paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail.http://dx.doi.org/10.1155/2019/9014217 |
| spellingShingle | Wansuo Liu Dengwei Wang Wenjun Shi Shape Prior Embedded Level Set Model for Image Segmentation Journal of Electrical and Computer Engineering |
| title | Shape Prior Embedded Level Set Model for Image Segmentation |
| title_full | Shape Prior Embedded Level Set Model for Image Segmentation |
| title_fullStr | Shape Prior Embedded Level Set Model for Image Segmentation |
| title_full_unstemmed | Shape Prior Embedded Level Set Model for Image Segmentation |
| title_short | Shape Prior Embedded Level Set Model for Image Segmentation |
| title_sort | shape prior embedded level set model for image segmentation |
| url | http://dx.doi.org/10.1155/2019/9014217 |
| work_keys_str_mv | AT wansuoliu shapepriorembeddedlevelsetmodelforimagesegmentation AT dengweiwang shapepriorembeddedlevelsetmodelforimagesegmentation AT wenjunshi shapepriorembeddedlevelsetmodelforimagesegmentation |