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

Author:

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

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

Reference20 articles.

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2. Saranyaraj D (2022) Image de-noising and edge segmentation using bilateral filtering and gabor-cut for edge representation of a breast tumor. In: 2022 international conference on engineering and emerging technologies (ICEET), Kuala Lumpur, Malaysia. IEEE, pp 1–6. https://doi.org/10.1109/ICEET56468.2022.10007228

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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