Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization

Joan M. Núñez, Jorge Bernal, Javier Sánchez, Fernando Vilariño

Abstract

This paper presents an approach to mitigate the contribution of blood vessels to the energy image used at different tasks of automatic colonoscopy image analysis. This goal is achieved by introducing a characterization of endoluminal scene objects which allows us to differentiate between the trace of 2-dimensional visual objects, such as vessels, and shades from 3-dimensional visual objects, such as folds. The proposed characterization is based on the influence that the object shape has in the resulting visual feature, and it leads to the development of a blood vessel attenuation algorithm. A database consisting of manually labelled masks was built in order to test the performance of our method, which shows an encouraging success in blood vessel mitigation while keeping other structures intact. Moreover, by extending our method to the only available polyp localization algorithm tested on a public database, blood vessel mitigation proved to have a positive influence on the overall performance.

References

  1. Ameling, S. et al. (2009). Texture-based polyp detection in colonoscopy. Bildverarbeitung für die Medizin 2009, pages 346-350.
  2. American Cancer Society (2012). What are the key statistics about colorectal cancer? [Online; accessed 7- September-2012].
  3. Arnold, M. et al. (2010). Automatic segmentation and inpainting of specular highlights for endoscopic imaging. Journal on Image and Video Processing, 2010:9.
  4. Arnold, M. et al. (2011). Quality Improvement of Endoscopy Videos. In Proceedings of the 8th IASTED International Conference on Biomedical Engineering, Insbruck, Austria.
  5. Bernal, J. et al. (2011). Colonoscopy Book 1: Towards Intelligent Systems for Colonoscopy. In-Tech.
  6. Bernal, J. et al. (2012). Towards automatic polyp detection with a polyp appearance model. Pattern Recognition, 45(9):3166 - 3182.
  7. Blinn, J. (1977). Models of light reflection for computer synthesized pictures. In ACM SIGGRAPH Computer Graphics, volume 11, pages 192-198. ACM.
  8. Bratko, I. et al. (1990). KARDIO: a study in deep and qualitative knowledge for expert systems. MIT Press.
  9. Chaudhuri, S. et al. (1989). Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on medical imaging, 8(3):263- 269.
  10. Dahyot, R., Vilarin˜o, F., and Lacey, G. (2008). Improving the quality of color colonoscopy videos. Journal on Image and Video Processing, 2008:1-7.
  11. De Haan, G. and Bellers, E. (1998). Deinterlacing-an overview. Proceedings of the IEEE, 86(9):1839-1857.
  12. Espona, L. et al. (2007). A snake for retinal vessel segmentation. Pattern Recognition and Image Analysis, pages 178-185.
  13. Gil, D. et al. (2009). Structure-preserving smoothing of biomedical images. In Computer Analysis of Images and Patterns, pages 427-434. Springer.
  14. Hassinger, J. et al. (2010). Effectiveness of a MultimediaBased Educational Intervention for Improving Colon Cancer Literacy in Screening Colonoscopy Patients. Diseases of the Colon & Rectum, 53(9):1301.
  15. Hoover, A. et al. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. Medical Imaging, IEEE Transactions on, 19(3):203-210.
  16. Imai, Y. et al. (2011). Estimation of multiple illuminants based on specular highlight detection. Computational Color Imaging, pages 85-98.
  17. Jiang, X. et al. (2003). Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(1):131-137.
  18. Joblove, G. and Greenberg, D. (1978). Color spaces for computer graphics. ACM SIGGRAPH Computer Graphics, 12(3):20-25.
  19. Machine Vision Group, CVC (2012). Cvc-colondb: A database for assessment of polyp detection. [Online; accessed 24-July-2012].
  20. Marín, D. et al. (2011). A new supervised method for blood vessel segmentation in retinal images by using graylevel and moment invariants-based features. Medical Imaging, IEEE Transactions on, 30(1):146-158.
  21. Mendonca, A. and Campilho, A. (2006). Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. Medical Imaging, IEEE Transactions on, 25(9):1200- 1213.
  22. Papari, G. and Petkov, N. (2011). Edge and line oriented contour detection: State of the art. Image and Vision Computing, 29(2-3):79-103.
  23. Segnan, N. et al. (2011). European guidelines for quality assurance in colorectal cancer screening and diagnosis. Luxembourg: Publications Office of the European Union.
  24. Shafer, S. (1985). Using color to separate reflection components. Color Research & Application, 10(4):210-218.
  25. Soares, J. et al. (2006). Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. Medical Imaging, IEEE Transactions on, 25(9):1214- 1222.
  26. Staal, J. et al. (2004). Ridge-based vessel segmentation in color images of the retina. Medical Imaging, IEEE Transactions on, 23(4):501-509.
  27. Tjoa, M. and Krishnan, S. (2003). Feature extraction for the analysis of colon status from the endoscopic images. BioMedical Engineering OnLine, 2(9):1-17.
  28. Tresca, A. (2010). The Stages of Colon and Rectal Cancer. New York Times (About.com), page 1.
  29. Wei, J. et al. (2011). Computer-aided detection of breast masses: Four-view strategy for screening mammography. Medical Physics, 38:1867.
  30. Xu, L. and Luo, S. (2010). A novel method for blood vessel detection from retinal images. BioMedical Engineering OnLine, 9(1):14.
  31. Zana, F. and Klein, J. (2001). Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. Image Processing, IEEE Transactions on, 10(7):1010-1019.
  32. Zhu, H. and Liang, Z. (2010). Improved Curvature Estimation for Shape Analysis in Computer-Aided Detection of Colonic Polyps. Beijing, China, page 19.
Download


Paper Citation


in Harvard Style

M. Núñez J., Bernal J., Sánchez J. and Vilariño F. (2013). Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 162-171. DOI: 10.5220/0004211601620171


in Bibtex Style

@conference{visapp13,
author={Joan M. Núñez and Jorge Bernal and Javier Sánchez and Fernando Vilariño},
title={Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={162-171},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004211601620171},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization
SN - 978-989-8565-47-1
AU - M. Núñez J.
AU - Bernal J.
AU - Sánchez J.
AU - Vilariño F.
PY - 2013
SP - 162
EP - 171
DO - 10.5220/0004211601620171