Cervical cancer histology image identification method based on texture and lesion area features

Abstract

The issue of an automated approach for detecting cervical cancer is proposed to improve the accuracy of recognition. Firstly, the cervical cancer histology source images are needed to use image preprocessing for reducing the impact brought by noise of images as well as the impact on subsequent precise feature extraction brought by irrelevant background. Secondly, the images are grouped into ten vertical images and the information of texture feature is extracted by Grey Level Co-occurrence Matrix (GLCM). GLCM is an effective tool to analyze the features of texture. The textures of different diseases in the source image of Cervical Cancer Histology (such as contrast, correlation, entropy, uniformity and energy, etc.) can all be obtained in this way. Thirdly, the image is segmented by using K-means clustering and Marker-controlled watershed Algorithm. And each vertical image is divided into three layers to calculate the areas of different layers. Based on GLCM and lesion area features, the tissues are investigated with segmentation by using Support Vector Machine (SVM) method. Finally, the results show that it is effective and feasible to recognize cervical cancer by automated approach and verified by experiment.

Keywords:

• Cervical cancer
• automated approach
• segmentation
• GLCM
• SVM

Acknowledgement

All authors declare that there is no conflict of interest regarding the publication of this paper. And the authors would like to thank anonymous reviewers and the editor for their valuable comments and helpful suggestions. And the authors would like to thank Prof. Fei for his supports that have improved the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Natural Science Foundation of Anhui Province under grant 1608085MF146, the Natural Science Research Program of Colleges and Universities of Anhui Province under grant KJ2016A062, the Visiting Study Foundation for Outstanding Young Talent of Anhui Educational Committee under grant gxfxZD2016108, and the Foundation for talented young people of Anhui Polytechnic University under grant 2016BJRC008.