Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos

Abstract

A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance defining specular highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages: segmentation and then classification of bright spot regions. The former defines a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; final regions provided depend on restrictions over contrast value. Non-specular regions are filtered through a classification stage performed by a linear SVM classifier using model-based features from each region. We introduce a new validation database with more than 25, 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology.

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Notes

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    http://www.cvc.uab.es/CVC-Colon/index.php/databases/.

References

  1. 1.

    Alsaleh, S.M., Aviles, A.I., Sobrevilla, P., Casals, A., Hahn, J.K.: Adaptive segmentation and mask-specific sobolev inpainting of specular highlights for endoscopic images. In: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, pp. 1196–1199. IEEE (2016)

  2. 2.

    Angelopoulou, E.: Specular highlight detection based on the fresnel reflection coefficient. In: Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, pp. 1–8. IEEE (2007)

  3. 3.

    Arnold, M., Ghosh, A., Ameling, S., Lacey, G.: Automatic segmentation and inpainting of specular highlights for endoscopic imaging. J. Image Video Process. 2010, 9 (2010)

    Google Scholar 

  4. 4.

    Asundi, A., Wensen, Z.: Fast phase-unwrapping algorithm based on a gray-scale mask and flood fill. Appl. Opt. 37(23), 5416–5420 (1998)

    Article  Google Scholar 

  5. 5.

    Bernal, J., Gil, D., Sánchez, C., Sánchez, F.J.: Discarding non informative regions for efficient colonoscopy image analysis. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 1–10. Springer (2014)

  6. 6.

    Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graph. 43, 99–111 (2015)

    Article  Google Scholar 

  7. 7.

    Bernal, J., Sánchez, F.J., Rodríguez de Miguel, C., Fernández-Esparrach, G.: Screening for Colorectal Cancer with Colonoscopy, vol. 1, chap. In: Building up the Future of Colonoscopy—A Synergy between Clinicians and Computer Scientists, pp. 109–141. InTech (2015)

  8. 8.

    Bernal, J., Sánchez, J., Vilarino, F.: Towards automatic polyp detection with a polyp appearance model. Pattern Recognit. 45(9), 3166–3182 (2012)

    Article  Google Scholar 

  9. 9.

    Bernal, J., Sánchez, J., Vilarino, F.: Impact of image preprocessing methods on polyp localization in colonoscopy frames. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 7350–7354. IEEE (2013)

  10. 10.

    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  11. 11.

    Fernández-Esparrach, G., Bernal, J., López-Cerón, M., Córdova, H., Sánchez-Montes, C., de Miguel, C.R., Sánchez, F.J.: Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy 48(09), 837–842 (2016)

  12. 12.

    Hafner, M., Brunauer, L., Payer, H., Resch, R., Gangl, A., Uhl, A., Wrba, F., Vécsei, A.: Computer-aided classification of zoom-endoscopical images using fourier filters. IEEE Trans. Inf. Technol. Biomed. 14(4), 958–970 (2010)

    Article  Google Scholar 

  13. 13.

    Hearst, M.A., Dumais, S.T., Osman, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Int. Syst. Appl. 13(4), 18–28 (1998)

    Article  Google Scholar 

  14. 14.

    Iakovidis, D.K., Koulaouzidis, A.: Software for enhanced video capsule endoscopy: challenges for essential progress. Nat. Rev. Gastroenterol. Hepatol. 12(3), 172–186 (2015)

    Article  Google Scholar 

  15. 15.

    Kudo, S.E., Wakamura, K., Ikehara, N., Mori, Y., Inoue, H., Hamatani, S.: Diagnosis of colorectal lesions with a novel endocytoscopic classification–a pilot study. Endoscopy 43(10), 869–875 (2011)

    Article  Google Scholar 

  16. 16.

    Linker, R., Kelman, E.: Apple detection in nighttime tree images using the geometry of light patches around highlights. Comput. Electr. Agric. 114, 154–162 (2015)

    Article  Google Scholar 

  17. 17.

    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)

    Article  Google Scholar 

  18. 18.

    Meziou, L., Histace, A., Precioso, F.: Alpha-divergence maximization for statistical region-based active contour segmentation with non-parametric pdf estimations. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pp. 861–864. IEEE (2012)

  19. 19.

    Nistér, D., Stewénius, H.: Linear time maximally stable extremal regions. Comput. Vis. ECCV 2008, 183–196 (2008)

    Google Scholar 

  20. 20.

    Núñez, J.M., Bernal, J., Ferrer, M., Vilariño, F.: Impact of keypoint detection on graph-based characterization of blood vessels in colonoscopy videos. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 22–33. Springer (2014)

  21. 21.

    Shao, F., Jiang, G., Yu, M., Ho, Y.S.: Highlight-detection-based color correction method for multiview images. ETRI J. 31(4), 448–450 (2009)

    Article  Google Scholar 

  22. 22.

    Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283–293 (2014)

    Article  Google Scholar 

  23. 23.

    Tajbakhsh, N., Gurudu, S.R., Liang, J.: Automated polyp detection in colonoscopy videos using shape and context information. IEEE Trans. Med. Imag. 35(2), 630–644 (2016)

    Article  Google Scholar 

  24. 24.

    Tan, R.T., Ikeuchi, K.: Separating reflection components of textured surfaces using a single image. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 178–193 (2005)

    Article  Google Scholar 

  25. 25.

    Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176–201 (1993)

    Article  Google Scholar 

  26. 26.

    Xu, S.C., Ye, X., Wu, Y., Zhang, S.: Highlight detection and removal based on chromaticity. In: Image Analysis and Recognition, pp. 199–206. Springer (2005)

  27. 27.

    Yang, Q., Wang, S., Ahuja, N.: Real-time specular highlight removal using bilateral filtering. Comput. Vis. ECCV 2010, 87–100 (2010)

    Google Scholar 

  28. 28.

    Yoon, K., Kweon, I.: Correspondence search in the presence of specular highlights using specular-free two-band images. Comput. Vis. ACCV 2006, 761–770 (2006)

    Google Scholar 

  29. 29.

    Zou, K.H., Warfield, S.K., Bharatha, A., Tempany, C.M., Kaus, M.R., Haker, S.J., Wells, W.M., Jolesz, F.A., Kikinis, R.: Statistical validation of image segmentation quality based on a spatial overlap index 1: scientific reports. Acad. Radiol. 11(2), 178–189 (2004)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Spanish Government through the funded project iVENDIS (DPI2015-65286-R), by the FSEED, by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, 2014-SGR-1470 and 2014-SGR-135 and by CERCA Programme / Generalitat de Catalunya.

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Correspondence to Jorge Bernal.

Appendix: Bright spot regions segmentation algorithms

Appendix: Bright spot regions segmentation algorithms

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Sánchez, F.J., Bernal, J., Sánchez-Montes, C. et al. Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos. Machine Vision and Applications 28, 917–936 (2017). https://doi.org/10.1007/s00138-017-0864-0

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Keywords

  • Specular highlights
  • Bright spot regions segmentation
  • Region classification
  • Colonoscopy