Segmentation of breast molybdenum target image lesions based on semi-supervised fuzzy clustering

被引:2
|
作者
Peng, Peng [1 ]
Wu, Danping [2 ]
Han, Fei-Chi [3 ]
Huang, Li-Jun [2 ]
Wei, Zhenlin [4 ]
Wang, Jie [2 ]
Jiang, Yizhang [1 ]
Xia, Kaijian [2 ,4 ]
机构
[1] Jiangnan Univ, Sch Artificial Intelligence & Comp Sci, Wuxi, Jiangsu, Peoples R China
[2] Soochow Univ, Changshu Hosp, Changshu, Jiangsu, Peoples R China
[3] Univ Idaho, Coll Engn, Moscow, ID USA
[4] Eighth Peoples Hosp, Nantong, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Medical image segmentation; semisupervised; fuzzy clustering algorithm; mammogram;
D O I
10.3233/JIFS-224458
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Currently, breast cancer is one of the most common cancers among women. To aid clinicians in diagnosis, lesion regions in mammography pictures can be segmented using an artificial intelligence system. This has significant clinical implications. Clustering algorithms, as unsupervised models, are widely used in medical image segmentation. However, due to the different sizes and shapes of lesions in mammography images and the low contrast between lesion areas and the surrounding pixels, it is difficult to use traditional unsupervised clustering methods for image segmentation. In this study, we try to apply the semisupervised fuzzy clustering algorithm to lesion segmentation in mammography molybdenum target images and propose semisupervised fuzzy clustering based on the cluster centres of labelled samples (called SFCM_V, where V stands for cluster centre). The algorithm refers to the cluster centre of the labelled sample dataset during the clustering process and uses the information of the labelled samples to guide the unlabelled samples during clustering to improve the clustering performance. We compare the SFCM_V algorithm with the current popular semisupervised clustering algorithm and an unsupervised clustering algorithm and perform experiments on real patient mammogram images using DICE and IoU as evaluation metrics; SFCM_V has the highest evaluation metric coefficient. Experiments demonstrate that SFCM_V has higher segmentation accuracy not only for larger lesion regions, such as tumours, but also for smaller lesion regions, such as calcified spots, compared with existing clustering algorithms.
引用
收藏
页码:9475 / 9493
页数:19
相关论文
共 50 条
  • [31] Semi-Supervised Fuzzy Clustering with Feature Discrimination
    Li, Longlong
    Garibaldi, Jonathan M.
    He, Dongjian
    Wang, Meili
    PLOS ONE, 2015, 10 (09):
  • [32] An improved semi-supervised fuzzy clustering algorithm
    Gao, Cui-Fang
    Wu, Xiao-Jun
    Zhang, Song-Shun
    Kongzhi yu Juece/Control and Decision, 2010, 25 (01): : 115 - 120
  • [33] Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering
    Kumar, Neeraj
    Uppala, Phanikrishna
    Duddu, Karthik
    Sreedhar, Had
    Varma, Vishal
    Guzman, Grace
    Walsh, Michael
    Sethi, Amit
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2019, 38 (05) : 1304 - 1313
  • [34] Fuzzy Semi-supervised Clustering with Target Clusters Using Different Additional Terms
    Miyamoto, Sadaaki
    Yamazaki, Mitsuaki
    Hashimoto, Wataru
    2009 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING ( GRC 2009), 2009, : 444 - 449
  • [35] Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation
    Wu, Chengmao
    Zhang, Jiajia
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2022, 13 (04) : 963 - 987
  • [36] Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation
    Chengmao Wu
    Jiajia Zhang
    International Journal of Machine Learning and Cybernetics, 2022, 13 : 963 - 987
  • [37] Semi-supervised Rock Image Segmentation and Recognition Based on Superpixel
    Liu Y.
    Lyu J.
    Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences, 2023, 55 (02): : 171 - 183
  • [38] Semi-supervised Ultrasound Image Segmentation Based on Curvelet Features
    Yun, Ting
    Xu, Yiqing
    Cao, Lin
    MEASUREMENT TECHNOLOGY AND ITS APPLICATION, PTS 1 AND 2, 2013, 239-240 : 104 - 114
  • [39] A TSVM based semi-supervised approach to SAR Image Segmentation
    Ji, Jun
    Shao, Fengjing
    Sun, Rencheng
    Zhang, Neng
    Liu, Guanfeng
    2008 INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND TRAINING AND 2008 INTERNATIONAL WORKSHOP ON GEOSCIENCE AND REMOTE SENSING, VOL 1, PROCEEDINGS, 2009, : 495 - 498
  • [40] Semi-supervised Probabilistic Relaxation for Image Segmentation
    Martinez-Uso, Adolfo
    Pla, Filiberto
    Sotoca, Jose M.
    Anaya-Sanchez, Henry
    PATTERN RECOGNITION AND IMAGE ANALYSIS: 5TH IBERIAN CONFERENCE, IBPRIA 2011, 2011, 6669 : 428 - 435