Computer-Aided Diagnosis (CAD) for Cervical Cancer Screening and Diagnosis: A New System Design in Medical Image Processing

  • Wenjing Li
  • Viara Van Raad
  • Jia Gu
  • Ulf Hansson
  • Johan Hakansson
  • Holger Lange
  • Daron Ferris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)


Uterine cervical cancer is the second most common cancer among women worldwide. Physicians visually inspect the cervix for certain distinctly abnormal morphologic features that indicate precursor lesions and invasive cancer. We introduce our vision of a Computer-Aided-Diagnosis (CAD) system for cervical cancer screening and diagnosis and provide the details of our system design and development process. The proposed CAD system is a complex multi-sensor, multi-data and multi-feature image analysis system. The feature set used in our CAD systems includes the same visual features used by physician and could be extended to new features introduced by new instrument technologies, like fluorescence spectroscopy. Preliminary results of our research on detecting the three most important features: blood vessel structures, acetowhite regions and lesion margins are shown.


Cervical Cancer Cervical Intraepithelial Neoplasia Cervical Cancer Screening Image Processing Algorithm Cervical Neoplasia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ferris, D., et al.: Modern Colposcopy, Textbook and Atlas, 2nd edn. (2002)Google Scholar
  2. 2.
    Dickman, E.D., et al.: Identification of cervical neoplasia using a simulation of human vision. Journal of Lower Genital Tract Disease 5(3), 144–152 (2001)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Mikhail, M.S., et al.: Computerized measurement of intercapillary distance using image analysis in women with cervical intraepithelial neoplasia: correlation with severity. Obstet. Gynocol. 95, 52–53 (2000)Google Scholar
  4. 4.
    Van Raad, V.: Active contour models-A multiscale implementation for anatomical feature delineation in cervical images. In: Proc. of ICIP, pp. 557–560 (2004)Google Scholar
  5. 5.
    Yang, S., et al.: A multispectral digital cervigram TM analyzer in the wavelet domain for early detection of cervical cancer. In: Proc. of SPIE on Medical Imaging, vol. 5370, pp. 1833–1844 (2004)Google Scholar
  6. 6.
    Gordon, S., et al.: Image segmentation of uterine cervix images for indexing in PACS. In: Proc of the 17th IEEE Symposium on Computer based Medical Systems (2004)Google Scholar
  7. 7.
    Ji, Q., et al.: Texture Analysis for classification of cervix lesions. IEEE Trans. on Medical Imaging 19(11), 1144–1149 (2000)CrossRefGoogle Scholar
  8. 8.
    Balas, C.: A novel optical imaging method for the early detection, quantitative grading and mapping of cancerous and precancerous lesions of cervix. IEEE Trans. on Biomedical Engineering 48(1), 96–104 (2001)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Orfanoudaki, I.M., et al.: A clinical study of optical biopsy of the uterine cervix using a multispectral imaging system. Gynecologic Oncology 96, 119–131 (2005)CrossRefGoogle Scholar
  10. 10.
    Claude, I., et al.: Contour features for colposcopic iamge classification by artificial neural networks. In: Proc. of ICPR, pp. 771–774 (2002)Google Scholar
  11. 11.
    Mitra, P., et al.: Staging of cervical cancer with soft computing. IEEE Trans. on Biomedical Imaging 47(7), 934–940 (2000)CrossRefGoogle Scholar
  12. 12.
    Tumer, K., et al.: Ensembles of radial basis function networks for spectroscopic detection of cervical precancer. IEEE Trans. on Biomedical Engineering 45(8) (1998)Google Scholar
  13. 13.
    Lange, H., et al.: Reflectance and fluorescence hyperspectral elastic image registration. In: Proc. of SPIE on Medical Imaging, vol. 5370 (2004)Google Scholar
  14. 14.
    Reid, R., et al.: Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infections from high-grade cervical intraepithelial neoplasia. Am. J. Obstet. Gynecol 153(6), 611–618 (1984)Google Scholar
  15. 15.
    Rubin, M.M., Barbo, D.M.: Ch.9a: Rubin and Barbo Colposcopic Assessment system. In: Colposcopy: Principles and practice, pp. 187–195. W.B. Saunders Company, Philadelphia (2002)Google Scholar
  16. 16.
    Gustafsson, U., et al.: Fluorescence and reflectance monitoring of human cervical tissue in vivo: a case study. In: Proc. of SPIE on Photonics West Biomedical Optics, vol. 4959 (2003)Google Scholar
  17. 17.
    Lange, H.: Automatic glare removal in reflectance imagery of the uterine cervix. In: Proc. of SPIE on Medical Imaging, vol. 5747 (2005)Google Scholar
  18. 18.
    Lange, H.: Automatic detection of multi-level Acetowhite regions in RGB color images of the uterine cervix. In: Proc. of SPIE on Medical Imaging, vol. 5747 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Wenjing Li
    • 1
  • Viara Van Raad
    • 1
  • Jia Gu
    • 1
  • Ulf Hansson
    • 1
  • Johan Hakansson
    • 1
  • Holger Lange
    • 1
  • Daron Ferris
    • 2
  1. 1.STI Medical SystemsHonoluluUSA
  2. 2.Medical College of GeorgiaAugustaUSA

Personalised recommendations