Automatic segmentation of cervical region in colposcopic images using K-means

  • Bing Bai
  • Pei-Zhong LiuEmail author
  • Yong-Zhao Du
  • Yan-Ming Luo
Special Issue Article


Colposcopy is an important imaging modality for the detection of cervical lesions. The analysis of colposcopic images, especially the effective segmentation of the cervical region, has important clinical value in clinical application. A cervical segmentation method based on the HSV color mode is proposed, which can divide and extract the cervical region in the medical and anatomical sense. Firstly, the histogram threshold method is used to analyze the histogram (Y) of the colposcopic image. In order to achieve the removal of the mirror reflection pretreatment operation in the colposcopy image. Secondly, the Preprocessed RGB images is used. Then, the colposcopic image is converted into the HSV color space, and the V component is extracted using the K-means algorithm. Finally, using the area filter to smooth the edge, the segmented cervical region can be obtained. In our study, 110 standard colposcopy images, which were tagged by experts, were tested and verified. The segmentation results were analyzed and compared using dice coefficients, Jaccard coefficients, structural segmentation accuracy specificity, sensitivity, positive predictive value, and negative predictive value. Our experimental results show that the accuracy, specificity and sensitivity of the method are 87.25%, 81.99% and 96.70%, respectively. The effectiveness of the method in clinical segmentation was verified. Our study has demonstrated that cervical regional segmentation of colposcopic images based on HSV color space using K-means has high clinical utility and can help medical specialists in the diagnosis of cervical cancer.


Colposcopy image Image segmentation Image mirror reflection HSV color space K-means algorithm 



We thank the Fujian Provincial Maternal and Child Health Hospital for providing the datasets used in this paper.


This work was supported by the Grants from National Natural Science Foundation of China (No. 61605048 and No. 61603144, and Grant 61403245 and Grant 91648119), Natural Science Foundation of Fujian Province, China (No. 2016J01300), Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University (No. ZQN-PY518), and the Scientific Research Funds of Huaqiao University (No. 15BS413), and the Young and Middle-aged Teachers Education Scientific Research Project of Fujian Province, China (No. JAT160020), Natural Science Foundation of Fujian Province, China (Grant No. 2015J01256), the Talent project of Huaqiao University (Grant No. 14BS215), and Quanzhou scientific and technological planning projects of Fujian, China (Grant Nos. 2015Z120, 2017G024), and the Subsidized Project for Postgraduates’ Innovative Fund in Scientific Research of Huaqiao University (No. 17014084001).

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflicts of interest.

Ethical approval

All experiments were performed in compliance with the ethical standards set by our institutional board.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  • Bing Bai
    • 1
  • Pei-Zhong Liu
    • 1
    • 3
    Email author
  • Yong-Zhao Du
    • 1
    • 3
  • Yan-Ming Luo
    • 2
  1. 1.College of EngineeringHuaqiao UniversityQuanzhouChina
  2. 2.College of Computer Science and TechnologyHuaqiao UniversityXiamenChina
  3. 3.Research Center of Apply Statistics and Big DataHuaqiao UniversityXiamenChina

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