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A Cervical Histopathology Image Clustering Approach Using Graph Based Features
- Source :
- SN Computer Science. 2
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
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.
Details
- ISSN :
- 26618907 and 2662995X
- Volume :
- 2
- Database :
- OpenAIRE
- Journal :
- SN Computer Science
- Accession number :
- edsair.doi...........f6bc76a2935b1cbf92b74b922ee7c4db