A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method
When the image is affected by strong noise and uneven intensity, the traditional active contour models often cannot obtain accurate results. In this paper, a novel adaptive fractional differential active contour image segmentation method is proposed to solve the above problem. At first, in order to...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Fractal and Fractional |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3110/6/10/579 |
| _version_ | 1797473207929798656 |
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| author | Yanzhu Zhang Lijun Yang Yan Li |
| author_facet | Yanzhu Zhang Lijun Yang Yan Li |
| author_sort | Yanzhu Zhang |
| collection | DOAJ |
| description | When the image is affected by strong noise and uneven intensity, the traditional active contour models often cannot obtain accurate results. In this paper, a novel adaptive fractional differential active contour image segmentation method is proposed to solve the above problem. At first, in order to extract more texture parts of the image, an adaptively fractional order matrix is constructed according to the gradient information of the image, varying the fractional order of each pixel. Then, the traditional edge-stopping function in the regularization term is susceptible to noise, and a new fractional-order edge-stopping function is designed to improve noise resistance. In this paper, a fitting term based on adaptive fractional differentiation is introduced to solve the problem of improper selection of the initial contour position leading to inaccurate segmentation results so that the initial contour position can be selected arbitrarily. Finally, the experimental results show that the proposed method can effectively improve the segmentation accuracy of noise images and weak-edge images and can arbitrarily select the position selection of the initial contour. |
| first_indexed | 2024-03-09T20:11:34Z |
| format | Article |
| id | doaj.art-4d9017940de3488aafceca36891377c1 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-09T20:11:34Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-4d9017940de3488aafceca36891377c12023-11-24T00:11:59ZengMDPI AGFractal and Fractional2504-31102022-10-0161057910.3390/fractalfract6100579A Novel Adaptive Fractional Differential Active Contour Image Segmentation MethodYanzhu Zhang0Lijun Yang1Yan Li2School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, ChinaSchool of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, ChinaWhen the image is affected by strong noise and uneven intensity, the traditional active contour models often cannot obtain accurate results. In this paper, a novel adaptive fractional differential active contour image segmentation method is proposed to solve the above problem. At first, in order to extract more texture parts of the image, an adaptively fractional order matrix is constructed according to the gradient information of the image, varying the fractional order of each pixel. Then, the traditional edge-stopping function in the regularization term is susceptible to noise, and a new fractional-order edge-stopping function is designed to improve noise resistance. In this paper, a fitting term based on adaptive fractional differentiation is introduced to solve the problem of improper selection of the initial contour position leading to inaccurate segmentation results so that the initial contour position can be selected arbitrarily. Finally, the experimental results show that the proposed method can effectively improve the segmentation accuracy of noise images and weak-edge images and can arbitrarily select the position selection of the initial contour.https://www.mdpi.com/2504-3110/6/10/579active contour modelimage segmentationfractional differential operatoredge-stopping functionlocal fitting variance |
| spellingShingle | Yanzhu Zhang Lijun Yang Yan Li A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method Fractal and Fractional active contour model image segmentation fractional differential operator edge-stopping function local fitting variance |
| title | A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method |
| title_full | A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method |
| title_fullStr | A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method |
| title_full_unstemmed | A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method |
| title_short | A Novel Adaptive Fractional Differential Active Contour Image Segmentation Method |
| title_sort | novel adaptive fractional differential active contour image segmentation method |
| topic | active contour model image segmentation fractional differential operator edge-stopping function local fitting variance |
| url | https://www.mdpi.com/2504-3110/6/10/579 |
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