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A Cervical Histopathology Image Clustering Approach Using Graph Based Features

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Abstract

To apply topological information to solve a cervical histopathology image clustering (CHIC) problem, a graph based unsupervised learning (GBUL) approach is proposed in this paper. First, the GBUL method applies color features and k-means clustering to carry out a first-stage “coarse” clustering. Then, a skeletonization based node generation (SBNG) approach is introduced to approximate the distribution of cervical cell nuclei. Thirdly, based on the SBNG nodes, multiple graphs are constructed. Next, graph features are extracted based on the constructed graphs. Finally, k-means clustering is used again for the second-stage clustering. In the experiment, a practical Hematoxylin–eosin staining cervical histopathology image dataset with 40 whole-slide imaging images is tested, obtaining a promising CHIC result and showing a huge potential in the cancer risk prediction field.

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Acknowledgements

We thank Zhijie Hu, due to his contribution is considered as important as the first author in this paper. We also thank Miss Zixian Li, Mr. Guoxian Li and B.Sc. Muhammad Mamunur Rahaman from the MIaMIA Group for their important discussion and proofreading, respectively.

Funding

This work is supported by the “National Natural Science Foundation of China” (no. 61806047) and the “Fundamental Research Funds for the Central Universities” (no. N2019003).

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Authors and Affiliations

  1. Research Group for Microscopic Image and Medical Image Analysis, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, People’s Republic of China

    Chen Li, Zhijie Hu, Hao Chen, Shiliang Ai & Jinghua Zhang

  2. Cancer Hospital of China Medical University, No. 44 Xiaoheyan Road, Dadong District, Shenyang, 110042, Liaoning, People’s Republic of China

    Yong Zhang, Qian Wang & Xiaoyan Li

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  1. Chen Li
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Correspondence to Xiaoyan Li.

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This article is part of the topical collection “Computational Biology and Biomedical Informatics” guest edited by Dhruba Kr Bhattacharyya, Sushmita Mitra and Jugal Kr Kalita.

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Li, C., Hu, Z., Chen, H. et al. A Cervical Histopathology Image Clustering Approach Using Graph Based Features. SN COMPUT. SCI. 2, 66 (2021). https://doi.org/10.1007/s42979-021-00469-z

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