Peng, Peng and Wu, Danping and Huang, Li-Jun and Wang, Jianqiang and Zhang, Li and Wu, Yue and Jiang, Yizhang and Lu, Zhihua and Lai, Khin Wee and Xia, Kaijian (2024) Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation. Interdisciplinary Sciences-Computational Life Sciences, 16 (1). pp. 39-57. ISSN 1913-2751, DOI https://doi.org/10.1007/s12539-023-00580-0.
Full text not available from this repository.Abstract
Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) (62171203), 333 High level personnel training project of Jiangsu Province, Changshu City Health and Health Committee Science and Technology Program (csws201913), Changshu Science and Technology Program (CS202015); (CS202246), Suzhou Key Supporting Subjects (SZFCXK202147), Technology Demonstration Project of Social Development of Jiangsu Province (BE2019631) |
| Uncontrolled Keywords: | Semi-supervised fuzzy clustering; Mammography; Segmentation of lesions; Weighting; A priori knowledge learning |
| Subjects: | R Medicine > R Medicine (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 15 Nov 2024 01:29 |
| Last Modified: | 15 Nov 2024 01:29 |
| URI: | http://eprints.um.edu.my/id/eprint/46033 |
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