Automatic segmentation of cervical region in colposcopic images using K-means
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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.
KeywordsColposcopy 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.
All experiments were performed in compliance with the ethical standards set by our institutional board.
Informed consent was obtained from all individual participants included in the study.
- 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.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.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
- 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.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.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
- 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.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
- 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.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
- 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
- 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
- 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
- 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.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