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
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