Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation-Reference-Cited by-同舟云学术

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation

Published:2023-07-24 Issue: Volume: Page:
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Peng Peng,Wu Danping,Huang Li-Jun,Wang Jianqiang,Zhang Li,Wu Yue,Jiang Yizhang,Lu Zhihua,Lai Khin Wee,Xia Kaijian^ORCID

Funder

Suzhou Key Supporting Subjects

Changshu Science and Technology Program

Changshu City Health and Health Committee Science and Technology Program

333 High level personnel training project of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-023-00580-0.pdf

Reference20 articles.

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3. Salih AM, Kamil MY (2018) Mammography image segmentation based on fuzzy morphological operations. In: 2018 1st annual international conference on information and sciences (AiCIS), Fallujah, Iraq. IEEE, pp 40–44. https://doi.org/10.1109/AiCIS.2018.00020

4. Ramudu K, Srinivas A, Girija SP, Reddy GR (2022) Segmentation of tumors in MRI brain images using modified PSO and ADF based SVM. In: 2022 first international conference on electrical, electronics, information and communication technologies (ICEEICT), Trichy, India. IEEE, pp 1–6. https://doi.org/10.1109/ICEEICT53079.2022.9768621

5. Cheng Y, Gao Y, Xie L, Xie X, Lin W (2022) Spatial enhanced rotation aware network for breast mass segmentation in digital mammogram. IEEE Access 10:92559–92570. https://doi.org/10.1109/ACCESS.2020.2978009

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