Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation
Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical i...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9044368/ |
| _version_ | 1830089952669466624 |
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| author | Asim Niaz Kaynat Rana Aditi Joshi Asad Munir Daeun Dana Kim Hyun Chul Song Kwang Nam Choi |
| author_facet | Asim Niaz Kaynat Rana Aditi Joshi Asad Munir Daeun Dana Kim Hyun Chul Song Kwang Nam Choi |
| author_sort | Asim Niaz |
| collection | DOAJ |
| description | Image inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy. |
| first_indexed | 2024-12-16T16:54:35Z |
| format | Article |
| id | doaj.art-83cc396ebf044558b85ea2568c914368 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:54:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-83cc396ebf044558b85ea2568c9143682022-12-21T22:23:55ZengIEEEIEEE Access2169-35362020-01-018573485736210.1109/ACCESS.2020.29824879044368Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image SegmentationAsim Niaz0https://orcid.org/0000-0003-3905-9774Kaynat Rana1Aditi Joshi2Asad Munir3Daeun Dana Kim4Hyun Chul Song5Kwang Nam Choi6https://orcid.org/0000-0002-7420-9216Department of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Electrical Engineering, University of Engineering and Technology, Taxila, PakistanDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Industrial and Information Engineering, Università degli Studi di Udine, Udine, ItalyDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, South KoreaImage inhomogeneity often occurs in real-world images and may present considerable difficulties during image segmentation. Therefore, this paper presents a new approach for the segmentation of inhomogeneous images. The proposed hybrid active contour model is formulated by combining the statistical information of both the local and global region-based energy fitting models. The inclusion of the local region-based energy fitting model assists in extracting the inhomogeneous intensity regions, whereas the curve evolution over the homogeneous regions is accelerated by including the global region-based model in the proposed method. Both the local and global region-based energy functions in the proposed model drag contours toward the accurate object boundaries with precision. Each of the local and global region-based parts are parameterized with weight coefficients, based on image complexity, to modulate two parts. The proposed hybrid model is strongly capable of detecting region of interests (ROIs) in the presence of complex object boundaries and noise, as its local region-based part comprises bias field. Moreover, the proposed method includes a new bias field (NBF) initialization and eliminates the dependence over the initial contour position. Experimental results on synthetic and real-world images, produced by the proposed model, and comparative analysis with previous state-of-the-art methods confirm its superior performance in terms of both time efficiency and segmentation accuracy.https://ieeexplore.ieee.org/document/9044368/Active contoursbias fieldimage segmentationintensity inhomogeneitylevel set |
| spellingShingle | Asim Niaz Kaynat Rana Aditi Joshi Asad Munir Daeun Dana Kim Hyun Chul Song Kwang Nam Choi Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation IEEE Access Active contours bias field image segmentation intensity inhomogeneity level set |
| title | Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation |
| title_full | Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation |
| title_fullStr | Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation |
| title_full_unstemmed | Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation |
| title_short | Hybrid Active Contour Based on Local and Global Statistics Parameterized by Weight Coefficients for Inhomogeneous Image Segmentation |
| title_sort | hybrid active contour based on local and global statistics parameterized by weight coefficients for inhomogeneous image segmentation |
| topic | Active contours bias field image segmentation intensity inhomogeneity level set |
| url | https://ieeexplore.ieee.org/document/9044368/ |
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