An adaptive level set method based on joint estimation dealing with intensity inhomogeneity
Abstract Automatic object segmentation has been a challenging task due to intensity inhomogeneity. The traditional way is to eliminate the intensity inhomogeneity, which causes the object to lose useful intensity information. The authors propose an adaptive level set method for the segmentation of i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-05-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12115 |
| _version_ | 1811266473855811584 |
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| author | Jiang Zhu Yan Zeng Jianqi Li Shujuan Tian Haolin Liu |
| author_facet | Jiang Zhu Yan Zeng Jianqi Li Shujuan Tian Haolin Liu |
| author_sort | Jiang Zhu |
| collection | DOAJ |
| description | Abstract Automatic object segmentation has been a challenging task due to intensity inhomogeneity. The traditional way is to eliminate the intensity inhomogeneity, which causes the object to lose useful intensity information. The authors propose an adaptive level set method for the segmentation of intensity inhomogeneous images. Firstly, global and local features are utilised to collaboratively estimate the image, which devotes to compensating for intensity inhomogeneity. The local estimation retains detailed spatial information, and the global estimation mainly contains the regional information of the partitioned object. Then, during the construction of the energy functional, joint estimation is introduced to create the external energy. To acquire the precise location of the boundary, a weighting factor indicated by the gradient is introduced into the internal energy. Finally, after the numerical calculation of the energy functional by additive operator splitting algorithm, this method achieves the desired performance in terms of accuracy and robustness. Experimental results verify this method outperforms the comparative methods and can be applied to many real‐world scenarios. |
| first_indexed | 2024-04-12T20:42:43Z |
| format | Article |
| id | doaj.art-883037e662a04a58b2d657e74024a390 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-12T20:42:43Z |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-883037e662a04a58b2d657e74024a3902022-12-22T03:17:21ZengWileyIET Image Processing1751-96591751-96672021-05-011571424143810.1049/ipr2.12115An adaptive level set method based on joint estimation dealing with intensity inhomogeneityJiang Zhu0Yan Zeng1Jianqi Li2Shujuan Tian3Haolin Liu4Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan ChinaKey Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan ChinaCollege of Automation and Electronic Information Xiangtan University Xiangtan ChinaKey Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan ChinaKey Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan ChinaAbstract Automatic object segmentation has been a challenging task due to intensity inhomogeneity. The traditional way is to eliminate the intensity inhomogeneity, which causes the object to lose useful intensity information. The authors propose an adaptive level set method for the segmentation of intensity inhomogeneous images. Firstly, global and local features are utilised to collaboratively estimate the image, which devotes to compensating for intensity inhomogeneity. The local estimation retains detailed spatial information, and the global estimation mainly contains the regional information of the partitioned object. Then, during the construction of the energy functional, joint estimation is introduced to create the external energy. To acquire the precise location of the boundary, a weighting factor indicated by the gradient is introduced into the internal energy. Finally, after the numerical calculation of the energy functional by additive operator splitting algorithm, this method achieves the desired performance in terms of accuracy and robustness. Experimental results verify this method outperforms the comparative methods and can be applied to many real‐world scenarios.https://doi.org/10.1049/ipr2.12115Optical, image and video signal processingComputer vision and image processing techniques |
| spellingShingle | Jiang Zhu Yan Zeng Jianqi Li Shujuan Tian Haolin Liu An adaptive level set method based on joint estimation dealing with intensity inhomogeneity IET Image Processing Optical, image and video signal processing Computer vision and image processing techniques |
| title | An adaptive level set method based on joint estimation dealing with intensity inhomogeneity |
| title_full | An adaptive level set method based on joint estimation dealing with intensity inhomogeneity |
| title_fullStr | An adaptive level set method based on joint estimation dealing with intensity inhomogeneity |
| title_full_unstemmed | An adaptive level set method based on joint estimation dealing with intensity inhomogeneity |
| title_short | An adaptive level set method based on joint estimation dealing with intensity inhomogeneity |
| title_sort | adaptive level set method based on joint estimation dealing with intensity inhomogeneity |
| topic | Optical, image and video signal processing Computer vision and image processing techniques |
| url | https://doi.org/10.1049/ipr2.12115 |
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