Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. Digital colposcopy is a promising and inexpensive technology for the detection of cervical intraepithelial neoplasia. However, diagnostic sensitivity varies widely depending on the doctor expertise. Therefore, automation of this process is needed in both, detection and visualization. Colposcopies cover four steps: macroscopic view with magnifier white light, observation under green light, Hinselmann and Schiller. Also, there are transition intervals where the specialist manipulates the observed area. In this paper, we focus on the temporal segmentation of the video in these steps. Using our solution, physicians may focus on the step of interest and lesion detection tools can determine the interval to diagnose. We solved the temporal segmentation problem using Weighted Automata. Images were described by their chromacity histograms and labeled using a KNN classifier with a precision of 97 %. Transition frames were recognized with a precision of 91 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guía global para la prevención y control del cáncer cervicouterino. Technical report, International Federation of Gynecology and Obstetrics, October 2009
Das, A., Kar, A., Bhattacharyya, D.: Elimination of specular reflection and identification of ROI: the first step in automated detection of cervical cancer using digital colposcopy. In: 2011 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 237–241. IEEE (2011)
Rouhbakhsh, F., Farokhi, F., Kangarloo, K.: Effective feature selection for pre-cancerous cervix lesions using artificial neural networks (2012)
Gordon, S., Zimmerman, G., Long, R., Antani, S., Jeronimo, J., Greenspan, H.: Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams. In: Medical Imaging, International Society for Optics and Photonics, 61444U–61444U (2006)
Alush, A., Greenspan, H., Goldberger, J.: Lesion detection and segmentation in uterine cervix images using an arc-level MRF. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 474–477. IEEE (2009)
Park, S.Y., Follen, M., Milbourne, A., Malpica, A., MacKinnon, N., Markey, M.K., Richards-Kortum, R., MacAulay, C., Rhodes, H.: Automated image analysis of digital colposcopy for the detection of cervical neoplasia. J. Biomed. Opt. 13(1), 014029–014029 (2008)
Acosta-Mesa, H.G., Zitova, B., Rios-Figueroa, H., Cruz-Ramirez, N., Marin-Hernandez, A., Hernandez-Jimenez, R., Cocotle-Ronzon, B.E., Hernandez-Galicia, E.: Cervical cancer detection using colposcopic images: a temporal approach. In: 2005 Sixth Mexican International Conference on Computer Science, ENC 2005, pp. 158–164. IEEE (2005)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)
Rabin, J., Delon, J., Gousseau, Y.: Circular earth mover’s distance for the comparison of local features. In: 2008 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)
Almagor, S., Boker, U., Kupferman, O.: What’s decidable about weighted automata? In: Bultan, T., Hsiung, P.-A. (eds.) ATVA 2011. LNCS, vol. 6996, pp. 482–491. Springer, Heidelberg (2011)
Acknowledgement
This work is financed by the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project UID/EEA/50014/2013.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fernandes, K., Cardoso, J.S., Fernandes, J. (2015). Temporal Segmentation of Digital Colposcopies. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-19390-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19389-2
Online ISBN: 978-3-319-19390-8
eBook Packages: Computer ScienceComputer Science (R0)