Skip to main content

Advertisement

Log in

Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

Visual inspection with acetic acid (VIA) is an effective, affordable and simple test for cervical cancer screening in resource-poor settings. But considerable expertise is needed to differentiate cancerous lesions from normal lesions, which is lacking in developing countries. Many studies have attempted automation of cervical cancer detection from cervix images acquired during the VIA process. These studies used images acquired through colposcopy or cervicography. However, colposcopy is expensive and hence is not feasible as a screening tool in resource-poor settings. Cervicography uses a digital camera to acquire cervix images which are subsequently sent to experts for evaluation. Hence, cervicography does not provide a real-time decision of whether the cervix is normal or not, during the VIA examination. In case the cervix is found to be abnormal, the patient may be referred to a hospital for further evaluation using Pap smear and/or biopsy. An android device with an inbuilt app to acquire images and provide instant results would be an obvious choice in resource-poor settings. In this paper, we propose an algorithm for analysis of cervix images acquired using an android device, which can be used for the development of decision support system to provide instant decision during cervical cancer screening. This algorithm offers an accuracy of 97.94%, a sensitivity of 99.05% and specificity of 97.16%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: GLOBOCAN 2012 v1.0, Cancer incidence and mortality worldwide: IARC Cancer Base No. 11 [Internet]. Lyon, France: IARC 2013. Available from: http://globocan.iarc.fr, accessed on 19/09/2017.

  2. Sankaranarayanan R, Wesley R, Somanathan T, Dhakad N: Visual inspection of the uterine cervix after the application of acetic acid in the detection of cervical carcinoma and its precursors. Cancer 83:2150–2156, 1998

    Article  CAS  PubMed  Google Scholar 

  3. Sankaranarayanan R, Shyamalakumary B, Wesley R, Sreedevi Amma N: Visual inspection with acetic acid in the early detection of cervical cancer and precursors. Int. J. Cancer 80:161–163, 1999

    Article  CAS  PubMed  Google Scholar 

  4. Denny L, Kuhn L, Pollack A, Wainwright H: Evaluation of alternative methods of cervical cancer screening for resource-poor settings. Cancer 89:826–833, 2000

    Article  CAS  PubMed  Google Scholar 

  5. Belinson JL, Pretorius RG, Zhang WH, Wu LY, Qiao YL, Elson P: Cervical cancer screening by simple visual inspection after acetic acid. Obstet Gynecol 98:441–444, 2001

    CAS  PubMed  Google Scholar 

  6. Sangwa-Lugoma G, Mahmud S, Nasr SH, Liaras J, Kayembe PK, Tozin RR, Drouin P, Lorincz A, Ferenczy A, Franco EL: Visual inspection as a cervical cancer screening method in a primary health-care setting in Africa. Int J Cancer 119:1389–1395, 2006

    Article  CAS  PubMed  Google Scholar 

  7. Holger Lange: Automatic glare removal in reflectance imagery of the uterine cervix. In Proc. SPIE 5747, Medical Imaging 2005: Image Processing, San Diego, California, United States 2005, pp 2183–2192.

  8. Othmane EM, Mustapha K, Hakim A, Taouq G, Yassir AB: Automatic detection and inpainting of specular reflections for colposcopic images. Cent. Eur. J. Comp. Sci. 1:341–354, 2011

    Google Scholar 

  9. Zimmerman-Moreno G, Greenspan H: Automatic detection of specular reflections in uterine cervical images. Proc. of SPIE Medical Imaging 6144, San Diego, California, United States, 2006, pp 2037–2045.

  10. Wenjing L, Jia G, Daron F, Allen P: Automated image analysis of uterine cervical images. In Proc. SPIE Medical Imaging 6214, San Diego, California, United States 2007, pp 65142P-9P.

  11. Gali Z, Shiri G, Hayit G: Automatic landmark detection in uterine cervical images for indexing in a content-retrieval system. In Proc. of IEEE International Symposium on Biomedical Imaging, Arlington, VA, USA 2006, pp 1348–1351.

  12. Greenspan H, Gordon S, Zimmerman G, Lotenberg S, Jeronimo J, Antani S, Long R: Automatic detection of anatomical landmarks in uterine cervical images. IEEE Trans Med Imaging 28:454–468, 2009

    Article  PubMed  Google Scholar 

  13. Abhishek D, Avijit K, Debasis B: Elimination of specular reflection and identification of ROI: the first step in automated detection of cervical cancer using digital colposcopy. In Proc. 2011 I.E. International Conference on Imaging Systems and Techniques (IST), Penang, Malaysia 2011, pp 237–241.

  14. Shelly L, Shiri G, Hayit G: Shape priors for segmentation of the cervix region within uterine cervical images. J. Digit. Imaging 22:286–296, 2009

    Article  Google Scholar 

  15. Lange H: Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix. In Proc. of SPIE Medical Imaging 5747, San Diego, California, United States 2005, pp 1004–1017.

  16. Park SY, Follen M, Milbourne A, Rhodes H, Malpica A, MacKinnon N, MacAulay C, Markey MK, Richards-Kortum R: Automated image analysis of digital colposcopy for the detection of cervical neoplasia. J. Biomed. Opt 13:014029, 2008

    Article  PubMed  Google Scholar 

  17. Li W, Venkataraman S, Gustafsson U, Oyama JC, Ferris DG, Lieberman RW: Using acetowhite opacity index for detecting cervical intraepithelial neoplasia. J. Biomed. Opt 14:014020, 2009

    Article  PubMed  Google Scholar 

  18. Rama Praba PS, Ranganathan H: Computerized lesion detection in colposcopy cervical images based on statistical features using Bayes classifier. In Proc. of the InConINDIA, AISC 132, Visakhapatnam, India 2012, pp 597–604.

  19. Alush A, Greenspan H, Goldberger J: Automated and interactive lesion detection and segmentation in uterine cervical images. IEEE Trans Med Imaging 29:488–501, 2010

    Article  PubMed  Google Scholar 

  20. Xu T, Kim E, Huang X: Adjustable adaboost classifier and pyramid features for image-based cervical cancer diagnosis. In Proc. International Symposium on Biomedical Imaging (ISBI), New York, NY, USA 2015, pp 281–285.

  21. Sukumar P, Gnanamurthy RK: Computer aided screening of cervical cancer using random forest classifier. RJPBCS 7:1521–1529, 2016

    Google Scholar 

  22. Ji Q, Engel J, Craine E: Texture analysis for classification of cervix lesions. IEEE Trans Med Imaging 19:1144–1149, 2000

    Article  CAS  PubMed  Google Scholar 

  23. Yeshwanth S, Brian N, Sunanda M, Sonal B: A unified model-based image analysis framework for automated detection of precancerous lesions in digitized uterine cervical images. IEEE J Select Top Signal Process 3:101–111, 2009

    Article  Google Scholar 

  24. Park SY, Sargent D, Liebeman R, Gustafsson U: Domain-specific image analysis for cervical neoplasia detection based on conditional random fields. IEEE Trans Med Imaging 30:867–878, 2011

    Article  PubMed  Google Scholar 

  25. Zhiyun X, Rodney LL, Sameer A, George RT: Automatic extraction of mosaic patterns in uterine cervical images. Computer-Based Medical Systems (CBMS) 2010 I.E. 23rd International Symposium, Perth, WA, 2000, pp 273–278.

  26. Song D, Edward K, Xiaolei H, Joseph P, Hctor MA, Je H: Multimodal entity coreference for cervical dysplasia diagnosis. IEEE Trans Med Imaging 34:229–245, 2015

    Article  PubMed  Google Scholar 

  27. Quinley KE, Gormley RH, Ratclie SJ, Shih T, Szep Z, Steiner A, Ramogola-Masire D, Kovarik CL: Use of mobile telemedicine for cervical cancer screening. J Telemed Telecare 17:203–209, 2011

    Article  PubMed  PubMed Central  Google Scholar 

  28. Catarino R, Vassilakos P, Scaringella S, Undurraga-Malinverno M, Meyer-Hamme U, Ricard-Gauthier D, Matute JC, Petignat P: Smartphone use for cervical cancer screening in low-resource countries: a pilot study conducted in Madagascar. PLoS ONE 10:1–10, 2015

    Google Scholar 

  29. Ricard-Gauthier D, Wisniak A, Catarino R, van Rossum AF, Meyer-Hamme U, Negulescu R, Scaringella S, Jinoro J, Vassilakos P, Petignat P: Use of smartphones as adjuvant tools for cervical cancer screening in low-resource settings. J Lower Genit Tract Dis 19:295–300, 2015

    Article  Google Scholar 

  30. Rashmi B, Vanita S, Radhika S, Niranjan K, Payal K, Sarif KN, Vidya C, Lovi G, Soubhik P: Feasibility of using mobile smartphone camera as an imaging device for screening of cervical cancer in a low-resource setting. J Postgrad Med Edu Res 50:69–74, 2016

    Article  Google Scholar 

  31. Kudva V, Prasad K, Guruvare S: Detection of specular reflection and segmentation of cervix region in uterine cervix images for cervical cancer screening. IRBM 38:218–291, 2017

    Article  Google Scholar 

  32. Claude I, Pouletaut P: Integrated color and texture tools for colposcopic image segmentation. In Proc. IEEE International Conference on Image Processing, Thessaloniki, Greece 2001, pp 311–314.

  33. Haralick RM, Shanmugan K, Dinstein I: Textural features for image classification. IEEE Trans. Syst., Man, Cybern, SMC 3:610–621, 1973

    Article  Google Scholar 

  34. Amadasun M, King R: Textural features corresponding to textural properties. IEEE Trans. Syst., Man, Cybern 19:1264–1274, 1989

    Article  Google Scholar 

  35. Sun C, Wee WG: Neighbouring gray level dependence matrix. Comput. Vision, Graphics Image Processing 23:341–352, 1982

    Article  Google Scholar 

  36. Ojala T, Pietikainen M, Maenpaa T: Multiresolution gray scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24:971–987, 2002

    Article  Google Scholar 

  37. Jouni P, Okko R, Serdar K: Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput Speech Lang. 29:145–171, 2015

    Article  Google Scholar 

  38. Ross Quinlan J: Induction of decision trees. Machine Learning 1:81–106, 1986

    Google Scholar 

  39. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108, 2015

    Article  PubMed  Google Scholar 

  40. Mishra GA, Pimple SA, Shastri SS: An overview of prevention and early detection of cervical cancers. Indian J. Med. Paediatr. Oncol 32:125–132, 2011

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the support of Dr. Suma Nair, Associate Professor, Community Medicine Department, Kasturba Medical College, Manipal, for facilitating the acquisition of images during the screening programmes conducted.

Funding

This publication is made possible by a subagreement from the Consortium for Affordable Medical Technologies (CAMTech) at Massachusetts General Hospital with funds provided by the generous support of the American people through the US Agency for International Development (USAID Grant number224581). The contents are the responsibility of Manipal Academy of Higher Education and do not necessarily reflect the views of Massachusetts General Hospital, USAID or the US Government.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Keerthana Prasad.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

Institutional Ethics Committee approval was obtained for this study, and an informed consent was obtained from the women participating in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kudva, V., Prasad, K. & Guruvare, S. Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings. J Digit Imaging 31, 646–654 (2018). https://doi.org/10.1007/s10278-018-0083-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-018-0083-x

Keywords

Navigation