Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster
Abstract Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-09-01
|
| Series: | IET Image Processing |
| Online Access: | https://doi.org/10.1049/ipr2.12530 |
| _version_ | 1811221508963434496 |
|---|---|
| author | Bao Feng Haoyang Zhou Jin Feng Yehang Chen Yu Liu Tianyou Yu Zhuangsheng Liu Wansheng Long |
| author_facet | Bao Feng Haoyang Zhou Jin Feng Yehang Chen Yu Liu Tianyou Yu Zhuangsheng Liu Wansheng Long |
| author_sort | Bao Feng |
| collection | DOAJ |
| description | Abstract Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting breast cancer lesions in DCE‐MRI images. First, based on the extreme learning machine (ELM) method, a robust function is proposed that combines image intensities and time‐domain features to enhance the difference between the lesions and other tissues. Second, an edge‐stop function (ESF) is introduced by combining the image intensity, time‐domain feature, and Hessian shape index to detect the irregular and blurred boundaries. At the boundary of breast cancer lesions, the energy function of ACM is minimized and the evolution of the contour curve completes, so the accurate lesion region of breast cancer can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) and Hausdorff distance (HD) of the segmentation of the proposed model in 50 samples are 85.88±6.62%, 75.72±9.68% and 11.62±4.72 mm, respectively. The results segmented by the proposed ACM are more similar to the manual segmentation than the compared models. |
| first_indexed | 2024-04-12T08:00:32Z |
| format | Article |
| id | doaj.art-ffefcf7f2e64430f996a2193a3eafbcc |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-12T08:00:32Z |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-ffefcf7f2e64430f996a2193a3eafbcc2022-12-22T03:41:21ZengWileyIET Image Processing1751-96591751-96672022-09-0116112947295810.1049/ipr2.12530Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means clusterBao Feng0Haoyang Zhou1Jin Feng2Yehang Chen3Yu Liu4Tianyou Yu5Zhuangsheng Liu6Wansheng Long7Department of Radiology Jiangmen Central Hospital Jiangmen Guangdong Province People's Republic of ChinaSchool of Electronic Information and Automation Guilin University of Aerospace Technology Guilin Guangxi Province People's Republic of ChinaDepartment of Student Financial Aid Management Center Guilin Normal College Guilin Guangxi Province People's Republic of ChinaSchool of Electronic Information and Automation Guilin University of Aerospace Technology Guilin Guangxi Province People's Republic of ChinaSchool of Electronic Information and Automation Guilin University of Aerospace Technology Guilin Guangxi Province People's Republic of ChinaSchool of Automation Science and Engineering South China University of Technology Guangzhou Guangdong Province People's Republic of ChinaDepartment of Radiology Jiangmen Central Hospital Jiangmen Guangdong Province People's Republic of ChinaDepartment of Radiology Jiangmen Central Hospital Jiangmen Guangdong Province People's Republic of ChinaAbstract Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting breast cancer lesions in DCE‐MRI images. First, based on the extreme learning machine (ELM) method, a robust function is proposed that combines image intensities and time‐domain features to enhance the difference between the lesions and other tissues. Second, an edge‐stop function (ESF) is introduced by combining the image intensity, time‐domain feature, and Hessian shape index to detect the irregular and blurred boundaries. At the boundary of breast cancer lesions, the energy function of ACM is minimized and the evolution of the contour curve completes, so the accurate lesion region of breast cancer can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) and Hausdorff distance (HD) of the segmentation of the proposed model in 50 samples are 85.88±6.62%, 75.72±9.68% and 11.62±4.72 mm, respectively. The results segmented by the proposed ACM are more similar to the manual segmentation than the compared models.https://doi.org/10.1049/ipr2.12530 |
| spellingShingle | Bao Feng Haoyang Zhou Jin Feng Yehang Chen Yu Liu Tianyou Yu Zhuangsheng Liu Wansheng Long Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster IET Image Processing |
| title | Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster |
| title_full | Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster |
| title_fullStr | Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster |
| title_full_unstemmed | Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster |
| title_short | Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster |
| title_sort | active contour model of breast cancer dce mri segmentation with an extreme learning machine and a fuzzy c means cluster |
| url | https://doi.org/10.1049/ipr2.12530 |
| work_keys_str_mv | AT baofeng activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster AT haoyangzhou activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster AT jinfeng activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster AT yehangchen activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster AT yuliu activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster AT tianyouyu activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster AT zhuangshengliu activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster AT wanshenglong activecontourmodelofbreastcancerdcemrisegmentationwithanextremelearningmachineandafuzzycmeanscluster |