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A Classification-Enhanced Vote Accumulation Scheme for Detecting Colonic Polyps

  • Nima Tajbakhsh
  • Suryakanth R. Gurudu
  • Jianming Liang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8198)

Abstract

Colorectal cancer most often begins as abnormal growth of the colon wall, commonly referred to as polyps. It has been shown that the timely removal of polyps with optical colonoscopy (OC) significantly reduces the incidence and mortality of colorectal cancer. However, a significant number of polyps are missed during OC in clinical practice—the pooled miss-rate for all polyps is 22% (95% CI, 19%–26%). Computer-aided detection may offer promises of reducing polyp miss-rate. This paper proposes a new automatic polyp detection method. Given a colonoscopy image, the main idea is to identify the edge pixels that lie on the boundary of polyps and then determine the location of a polyp from the identified edges. To do so, we first use the Canny edge detector to form a crude set of edge pixels, and then apply a set of boundary classifiers to remove a large portion of irrelevant edges. The polyp locations are then determined by a novel vote accumulation scheme that operates on the positively classified edge pixels. We evaluate our method on 300 images from a publicly available database and obtain results superior to the state-of-the-art performance.

Keywords

Optical colonoscopy polyp detection voting scheme random forest boundary classification 

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References

  1. 1.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2013. CA: A Cancer Journal for Clinicians 63(1), 11–30 (2013)CrossRefGoogle Scholar
  2. 2.
    Rabeneck, L., El-Serag, H., Davila, J., Sandler, R.: Outcomes of colorectal cancer in the united states: no change in survival (1986-1997). The American Journal of Gastroenterology 98(2), 471 (2003)Google Scholar
  3. 3.
    Winawer, S.J., Zauber, A.G., Ho, M.N., et al.: Prevention of colorectal cancer by colonoscopic polypectomy. New England Journal of Medicine 329(27), 1977–1981 (1993)CrossRefGoogle Scholar
  4. 4.
    Heresbach, D., Barrioz, T., Lapalus, M.G., Coumaros, D., et al.: Miss rate for colorectal neoplastic polyps: a prospective multicenter study of back-to-back video colonoscopies. Endoscopy 40(4), 284–290 (2008)CrossRefGoogle Scholar
  5. 5.
    van Rijn, J., Reitsma, J., Stoker, J., Bossuyt, P., van Deventer, S., Dekker, E.: Polyp miss rate determined by tandem colonoscopy: a systematic review. American Journal of Gastroenterology 101(2), 343–350 (2006)CrossRefGoogle Scholar
  6. 6.
    Bressler, B., Paszat, L.F., Chen, Z., Rothwell, D.M., Vinden, C., Rabeneck, L.: Rates of new or missed colorectal cancers after colonoscopy and their risk factors: A population-based analysis. Gastroenterology 132(1), 96–102 (2007), http://www.sciencedirect.com/science/article/pii/S001650850602261X CrossRefGoogle Scholar
  7. 7.
    Hewett, D.G., Kahi, C.J., Rex, D.K.: Does colonoscopy work? Journal of the National Comprehensive Cancer Network 8(1), 67–77 (2010)Google Scholar
  8. 8.
    CVC-Databasecolon: A database for assessment of polyp detection (2011), http://mv.cvc.uab.es/projects/colon-qa/cvccolondb
  9. 9.
    Karkanis, S., Iakovidis, D., Maroulis, D., Karras, D., Tzivras, M.: Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Transactions on Information Technology in Biomedicine 7(3), 141–152 (2003)CrossRefGoogle Scholar
  10. 10.
    Park, S.Y., Sargent, D., Spofford, I., Vosburgh, K., A-Rahim, Y.: A colon video analysis framework for polyp detection. IEEE Transactions on Biomedical Engineering 59(5), 1408–1418 (2012)CrossRefGoogle Scholar
  11. 11.
    Iakovidis, D.K., Maroulis, D.E., Karkanis, S.A.: An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy. Computers in Biology and Medicine 36(10), 1084–1103 (2006)CrossRefGoogle Scholar
  12. 12.
    Hwang, S., Oh, J., Tavanapong, W., Wong, J., de Groen, P.: Polyp detection in colonoscopy video using elliptical shape feature. In: IEEE International Conference on Image Processing, ICIP 2007, vol. 2, pp. II-465–II-468 (2007)Google Scholar
  13. 13.
    Bernal, J., Sánchez, J., Vilariño, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recognition 45(9), 3166–3182 (2012)CrossRefGoogle Scholar
  14. 14.
    Mordohai, P., Medioni, G.: Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning. Synthesis Lectures on Image, Video, and Multimedia Processing. Morgan & Claypool Publishers (2007), http://books.google.com/books?id=uvwxw5sJKywC
  15. 15.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  16. 16.
    Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nima Tajbakhsh
    • 1
  • Suryakanth R. Gurudu
    • 2
  • Jianming Liang
    • 1
  1. 1.Department of Biomedical InformaticsArizona State UniversityScottsdaleUSA
  2. 2.Division of Gastroenterology and Hepatology, Mayo ClinicScottsdaleUSA

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