Advertisement

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
  • 65 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgements

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

Funding

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.

References

  1. 1.
    Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F et al (2016) Cancer statistics in china 2015. CA Cancer J Clin 66(2):115–132CrossRefGoogle Scholar
  2. 2.
    Pfaendler KS, Wenzel L, Mechanic MB, Penner KR (2015) Cervical cancer survivorship: long-term quality of life and social support. Clin Therapeut 37:39–48CrossRefGoogle Scholar
  3. 3.
    Dempsey C, Govindarajulu G, Sridharan S et al (2014) Implications for dosimetric changes when introducing MR-guided brachytherapy for small volume cervix cancer: a comparison of CT and MR-based treatments in a single centre. Australas Phys Eng Sci Med 37(4):705–712CrossRefGoogle Scholar
  4. 4.
    Cronje HS (2004) Screening for cervical cancer in developing countries. Int J Gynecol Obstet 84:101–108CrossRefGoogle Scholar
  5. 5.
    Sun YP, Sargent D, Lieberman R, Gustafsson U (2011) Domain-specific image analysis for cervical neoplasia detection based on conditional random fields. IEEE Trans Med Imaging 30:867–878CrossRefGoogle Scholar
  6. 6.
    Sun YP, Sargent D, Lieberman R et al (2011) Domain-specific image analysis for cervical neoplasia detection based on conditional random fields. IEEE Trans Med Imaging 30(3):867–878CrossRefGoogle Scholar
  7. 7.
    Gordon S, Zimmerman G, Greenspan H (2004) Image segmentation of uterine cervix images for indexing in PACS. In: Proceedings of the IEEE symposium on computer-based medical systems, pp 298–298Google Scholar
  8. 8.
    Li W, Poirson A (2006) Detection and characterization of abnormal vascular patterns in automated cervical image analysis, Advances in Visual Computing. Springer, Berlin, pp 627–636Google Scholar
  9. 9.
    Van Raad V (2003) Design of Gabor wavelets for analysis of texture features in cervical images. Engineering in Medicine and Biology Society. In: Proceedings of the international conference of the IEEE, pp 806–809Google Scholar
  10. 10.
    Liu J, Li L, Wang L (2018) Acetowhite region segmentation in uterine cervix images using a registered ratio image. Comput Biol Med 93:47–55CrossRefGoogle Scholar
  11. 11.
    Obukhova NA, Motyko AA, Kang U et al (2017) Automated image analysis in multispectral system for cervical cancer diagnostic. In: Conference of Open Innovations Association, pp 345–351Google Scholar
  12. 12.
    Patil DB, Gaikwad MS, Singh DK et al (2016) Semi-automated lession grading in cervix images with Specular Reflection removal. In: International conference on inventive computation technologies, pp 1–5Google Scholar
  13. 13.
    Huang S, Gao M, Yang D et al (2015) Unbalanced graph-based transduction on superpixels for automatic cervigram image segmentation. In: IEEE international symposium on biomedical imaging, IEEE, pp 1556–1559Google Scholar
  14. 14.
    Praba PSR, Ranganathan H (2013) Wavelet transform based automatic lesion detection in cervix images using active contour. J Comput Sci 9(1):30–36CrossRefGoogle Scholar
  15. 15.
    Wang W (2010) Cervigram image segmentation based on reconstructive sparse representations. Med Imaging Image Process.  https://doi.org/10.1117/12.845461 CrossRefGoogle Scholar
  16. 16.
    Xue Z, Antani S, Long LR et al (2007) Comparative performance analysis of cervix ROI extraction and specular reflection removal algorithms for uterine cervix image analysis. Proc SPIE 6512(1):187–189Google Scholar
  17. 17.
    Karapetyan G, Sarukhanyan H (2013) Automatic detection and concealment of specular reflections for endoscopic images. In: Computer science and information technologies, IEEE, pp. 1–8Google Scholar
  18. 18.
    Guillemot C, Meur OL (2013) Image inpainting: overview and recent advances. IEEE Signal Process Mag 31(1):127–144CrossRefGoogle Scholar
  19. 19.
    Castle PE, Stoler MH, Solomon D et al (2007) The relationship of community biopsy-diagnosed cervical intraepithelial neoplasia grade 2 to the quality control pathology-reviewed diagnoses: an ALTS report. Am J Clin Pathol 127(5):805–815CrossRefGoogle Scholar
  20. 20.
    Corrêa FM, Russomano FB, Oliveira CA (2012) Colposcopic triage methods for detecting cervical intraepithelial neoplasia grade 3 after cytopathological diagnosis of low-grade squamous intraepithelial lesion: a systematic review on diagnostic tests. São Paulo Med J Revista paulista de medicina 130(1):44–52CrossRefGoogle Scholar
  21. 21.
    Xue Z, Antani S, Long LR et al (2007) Comparative performance analysis of cervix ROI extraction and specular reflection removal algorithms for uterine cervix image analysis. Medical Imaging 2007: Image Processing. International Society for Optics and Photonics, pp 187–189Google Scholar
  22. 22.
    Maheswari GU, Ramar K, Manimegalai D et al (2011) Short communication: an adaptive region based color texture segmentation using fuzzified distance metric. Appl Soft Comput J 11(2):2916–2924CrossRefGoogle Scholar
  23. 23.
    Stehle TH (2006) Specular reflection removal in endoscopic images. In: Proceedings of the 10th international student conference on electrical engineering, vol 10, pp 1–6Google Scholar
  24. 24.
    Mohd Ali N, Md Rashid NKA, Mustafah YM (2016) Performance comparison between RGB and HSV color segmentations for road signs detection. Appl Mech Mater 393:550–555CrossRefGoogle Scholar
  25. 25.
    Luo M, Ma YF, Zhang HJ (2003) A spatial constrained K-means approach to image segmentation. Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. IEEE, vol 2, pp 738–742Google Scholar
  26. 26.
    Siddiqui FU, Isa NAM (2011) Enhanced moving K-means (EMKM) algorithm for image segmentation. IEEE Trans Consum Electron 57(2):833–841CrossRefGoogle Scholar
  27. 27.
    Real R, Vargas JM (1996) The probabilistic basis of Jaccard’s Index of similarity. Syst Biol 45(3):380–385CrossRefGoogle Scholar
  28. 28.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRefGoogle Scholar
  29. 29.
    Gerig G, Jomier M, Chakos M (2001) Valmet: a new validation tool for assessing and improving 3D object segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 516–523Google Scholar
  30. 30.
    Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, Hirsch BE, Woodburn J (2006) A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph 30(2):75–87CrossRefGoogle Scholar
  31. 31.
    Panigrahy D, Sahu PK (2017) Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal. Australas Phys Eng Sci Med 40(1):191–207CrossRefGoogle Scholar
  32. 32.
    Saint-Pierre CA, Boisvert J, Grimard G, Cheriet F (2011) Detection and correction of specular reflections for automatic surgical tool segmentation in thoracoscopic images. Mach Vision Appl 22(1):171–180CrossRefGoogle Scholar
  33. 33.
    Proença H, Alexandre LA (2010) Iris recognition: analysis of the error rates regarding the accuracy of the segmentation stage. Image Vision Comput 28(1):202–206CrossRefGoogle Scholar
  34. 34.
    Das A, Kar A, Bhattacharyya D (2011) Elimination of specular reflection and identification of ROI: the first step in automated detection of Cervical Cancer using Digital Colposcopy. IEEE Int Conf Imaging Syst Techn 5(3), 237–241Google Scholar
  35. 35.
    Traversi M, Falagario M, Guaragnella C (2014) CADdy—Colposcopy learning machine for computer aided diagnosis. In: IEEE third international conference on consumer electronics, Berlin, IEEE, vol 7(6), pp 1–4Google Scholar

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

Personalised recommendations