A Multi-Source Circular Geodesic Voting Model for Image Segmentation
Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object inf...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1099-4300/26/12/1123 |
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| author | Shuwang Zhou Minglei Shu Chong Di |
| author_facet | Shuwang Zhou Minglei Shu Chong Di |
| author_sort | Shuwang Zhou |
| collection | DOAJ |
| description | Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters. To overcome these challenges, we propose a novel segmentation approach, named PolarVoting, which combines the minimal path encoding rich geometric features and CNNs which can provide efficient initialization. The introduced model involves two main steps: firstly, we leverage the PolarMask model to extract multiple source points for initialization, and secondly, we construct a voting score map which implicitly contains the segmentation mask via a modified circular geometric voting (CGV) scheme. This map embeds global geometric information for finding accurate segmentation. By integrating neural network representation with geometric priors, the PolarVoting model enhances segmentation accuracy and robustness. Extensive experiments on various datasets demonstrate that the proposed PolarVoting method outperforms both PolarMask and traditional single-source CGV models. It excels in challenging imaging scenarios characterized by intensity inhomogeneity, noise, and complex backgrounds, accurately delineating object boundaries and advancing the state of image segmentation. |
| format | Article |
| id | doaj-art-697a730e42ae464ca00b30da454b0054 |
| institution | DOAJ |
| issn | 1099-4300 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-697a730e42ae464ca00b30da454b00542025-08-20T02:57:13ZengMDPI AGEntropy1099-43002024-12-012612112310.3390/e26121123A Multi-Source Circular Geodesic Voting Model for Image SegmentationShuwang Zhou0Minglei Shu1Chong Di2College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaShandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaImage segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters. To overcome these challenges, we propose a novel segmentation approach, named PolarVoting, which combines the minimal path encoding rich geometric features and CNNs which can provide efficient initialization. The introduced model involves two main steps: firstly, we leverage the PolarMask model to extract multiple source points for initialization, and secondly, we construct a voting score map which implicitly contains the segmentation mask via a modified circular geometric voting (CGV) scheme. This map embeds global geometric information for finding accurate segmentation. By integrating neural network representation with geometric priors, the PolarVoting model enhances segmentation accuracy and robustness. Extensive experiments on various datasets demonstrate that the proposed PolarVoting method outperforms both PolarMask and traditional single-source CGV models. It excels in challenging imaging scenarios characterized by intensity inhomogeneity, noise, and complex backgrounds, accurately delineating object boundaries and advancing the state of image segmentation.https://www.mdpi.com/1099-4300/26/12/1123geodesic votingimage segmentationmulti-sourcepolar representationgeodesic model |
| spellingShingle | Shuwang Zhou Minglei Shu Chong Di A Multi-Source Circular Geodesic Voting Model for Image Segmentation Entropy geodesic voting image segmentation multi-source polar representation geodesic model |
| title | A Multi-Source Circular Geodesic Voting Model for Image Segmentation |
| title_full | A Multi-Source Circular Geodesic Voting Model for Image Segmentation |
| title_fullStr | A Multi-Source Circular Geodesic Voting Model for Image Segmentation |
| title_full_unstemmed | A Multi-Source Circular Geodesic Voting Model for Image Segmentation |
| title_short | A Multi-Source Circular Geodesic Voting Model for Image Segmentation |
| title_sort | multi source circular geodesic voting model for image segmentation |
| topic | geodesic voting image segmentation multi-source polar representation geodesic model |
| url | https://www.mdpi.com/1099-4300/26/12/1123 |
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