Affiliations: [a] Key Laboratory of Laser Life Science, Ministry of Education, College of Biophotonics, South China Normal University, Guangzhou, Guangdong, China | [b] GuangZhou Woman and Children’s Medical Center, Guangzhou, Guangzhou, China | [c] SCNU Qingyuan Institute of Science and Technology Innovation Co. Ltd., South China Normal University, Qingyuan, Guandong, China
Correspondence:
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Corresponding authors: Tongsheng Chen, Key Laboratory of Laser Life Science, Ministry of Education, College of Biophotonics, South China Normal University, Guangzhou, Guangdong 510631, China. E-mail: chentsh@scnu.edu.cn. Shenjiao Huang, GuangZhou Woman and Children’s Medical Center, Guangzhou, Guangdong 510623, China. E-mail: huangsj1229@hotmail.com.
Abstract: BACKGROUND: Cervical histopathology image classification is a crucial indicator in cervical biopsy results. OBJECTIVE: The objective of this study is to identify histopathology images of cervical cancer at an early stage by extracting texture and morphological features for the Support Vector Machine (SVM) classifier. METHODS: We extract three different texture features and one morphological feature of cervical histopathology images: first-order histogram, K-means clustering, Gray Level Co-occurrence Matrix (GLCM) and nucleus feature. The original dataset used in our experiment is obtained from 20 patients diagnosed with cervical cancer, including 135 whole slide images (WSIs). Given an entire WSI, the patches on its tissue region are extracted randomly. RESULTS: We finally obtain 3,000 patches, including 1,000 normal, 1,000 hysteromyoma and 1,000 cancer images. Among them, 80% of the entire data set is randomly selected as training set and the remaining 20% as test set. The accuracy of SVM classification using first-order histogram, K-means clustering, GLAM and nucleus feature for extracting features are respectively 87.4%, 90.6%, 91.6% and 93.5%. CONCLUSIONS: The classification accuracy of the SVM combining the four features is 96.8%, and the proposed nucleus feature plays a key role in the SVM classification of cervical histopathology images.
