Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation
This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8765727/ |
| _version_ | 1823966082590834688 |
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| author | Jiangxiong Fang Huaxiang Liu Liting Zhang Jun Liu Hesheng Liu |
| author_facet | Jiangxiong Fang Huaxiang Liu Liting Zhang Jun Liu Hesheng Liu |
| author_sort | Jiangxiong Fang |
| collection | DOAJ |
| description | This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF. |
| first_indexed | 2024-12-17T18:07:31Z |
| format | Article |
| id | doaj.art-c73fa22563144a47b03100b52c559482 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T18:07:31Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c73fa22563144a47b03100b52c5594822022-12-21T21:38:06ZengIEEEIEEE Access2169-35362019-01-017974929750410.1109/ACCESS.2019.29296598765727Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image SegmentationJiangxiong Fang0https://orcid.org/0000-0002-8960-9941Huaxiang Liu1Liting Zhang2Jun Liu3Hesheng Liu4Jiangxi Province Key Laboratory for Digital Land, East China University of Technology, Nanchang, ChinaJiangxi Province Key Laboratory for Digital Land, East China University of Technology, Nanchang, ChinaJiangxi Province Key Laboratory for Digital Land, East China University of Technology, Nanchang, ChinaSchool of Geophysics and Measure Control Technology, East China University of Technology, Nanchang, ChinaJiangxi Province Key Laboratory for Digital Land, East China University of Technology, Nanchang, ChinaThis study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF.https://ieeexplore.ieee.org/document/8765727/Image segmentationactive contoursigned pressure forceintensity inhomogeneity |
| spellingShingle | Jiangxiong Fang Huaxiang Liu Liting Zhang Jun Liu Hesheng Liu Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation IEEE Access Image segmentation active contour signed pressure force intensity inhomogeneity |
| title | Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation |
| title_full | Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation |
| title_fullStr | Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation |
| title_full_unstemmed | Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation |
| title_short | Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation |
| title_sort | active contour driven by weighted hybrid signed pressure force for image segmentation |
| topic | Image segmentation active contour signed pressure force intensity inhomogeneity |
| url | https://ieeexplore.ieee.org/document/8765727/ |
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