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Impact of Keypoint Detection on Graph-Based Characterization of Blood Vessels in Colonoscopy Videos

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Computer-Assisted and Robotic Endoscopy (CARE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8899))

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Abstract

We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of \(25\,\%\) of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.

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References

  1. Bressler, B., Paszat, L., Chen, Z., Rothwell, D., 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)

    Article  Google Scholar 

  2. Pudzs, M., Fuksis, R., Greitans, M.: Palmprint image processing with non-halo complex matched filters for forensic data analysis. In: International Workshop on Biometrics and Forensics (IWBF), pp. 1–4. IEEE (2013)

    Google Scholar 

  3. Azzopardi, G., Petkov, N.: Automatic detection of vascular bifurcations in segmented retinal images using trainable cosfire filters. Pattern Recogn. Lett. 34(8), 922–933 (2013)

    Article  Google Scholar 

  4. Bhuiyan, A., Nath, B., Chua, J., Ramamohanarao, K.: Automatic detection of vascular bifurcations and crossovers from color retinal fundus images. In: Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, SITIS’07, pp. 711–718. IEEE (2007)

    Google Scholar 

  5. Saha, S., Dutta Roy, N.: Automatic detection of bifurcation points in retinal fundus images. Int. J. Latest Res. Sci. Technol. 2(2), 105–108 (2013)

    Google Scholar 

  6. Can, A., Stewart, C.V., Roysam, B., Tanenbaum, H.L.: A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 347–364 (2002)

    Article  Google Scholar 

  7. Tsai, C.-L., Stewart, C.V., Tanenbaum, H.L., Roysam, B.: Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images. IEEE Trans. Inf. Technol. Biomed. 8(2), 122–130 (2004)

    Article  Google Scholar 

  8. Mahé, P., Ueda, N., Akutsu, T., Perret, J.-L., Vert, J.-P.: Graph kernels for molecular structure-activity relationship analysis with support vector machines. J. Chem. Inf. Model. 45(4), 939–951 (2005)

    Article  Google Scholar 

  9. Schenker, A., Bunke, H., Last, M., Kandel, A.: Graph-Theoretic Techniques for Web Content Mining. World Scientific, London (2005)

    Google Scholar 

  10. Harchaoui, Z., Bach, F.: Image classification with segmentation graph kernels. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR’07, pp. 1–8. IEEE (2007)

    Google Scholar 

  11. Llados, J., Sanchez, G.: Graph matching versus graph parsing in graphics recognitiona combined approach. Int. J. Pattern Recogn. Artif. Intell. 18(03), 455–473 (2004)

    Article  Google Scholar 

  12. Sanfeliu, A., Fu, K.-S.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern. 3, 353–362 (1983)

    Article  Google Scholar 

  13. Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recogn. Lett. 1(4), 245–253 (1983)

    Article  MATH  Google Scholar 

  14. Neuhaus, M., Bunke, H.: A graph matching based approach to fingerprint classification using directional variance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 191–200. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Ambauen, R., Fischer, S., Bunke, H.: Graph edit distance with node splitting and merging, and its application to diatom identification. In: Hancock, E.R., Vento, M. (eds.) GbRPR 2003. LNCS, vol. 2726, pp. 95–106. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  16. Robles-Kelly, A., Hancock, E.R.: Graph edit distance from spectral seriation. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 365–378 (2005)

    Article  Google Scholar 

  17. Boeres, M.C., Ribeiro, C.C., Bloch, I.: A randomized heuristic for scene recognition by graph matching. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 100–113. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  18. Sorlin, S., Solnon, C.: Reactive tabu search for measuring graph similarity. In: Brun, L., Vento, M. (eds.) GbRPR 2005. LNCS, vol. 3434, pp. 172–182. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  19. Justice, D., Hero, A.: A binary linear programming formulation of the graph edit distance. IEEE Trans. Pattern Anal. Mach. Intell. 28(8), 1200–1214 (2006)

    Article  Google Scholar 

  20. Neuhaus, M., Riesen, K., Bunke, H.: Fast suboptimal algorithms for the computation of graph edit distance. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 163–172. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  21. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(7), 950–959 (2009)

    Article  Google Scholar 

  22. Burkard, R., Dell’Amico, M., Martello, S.: Assignment Problems. SIAM, Philadelphia (2012). (Revised Reprint)

    Book  Google Scholar 

  23. Neuhaus, M.: Self-organizing graph edit distance. In: Hancock, E.R., Vento, M. (eds.) GbRPR 2003. LNCS, vol. 2726, pp. 83–94. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  24. Neuhaus, M., Bunke, H.: A probabilistic approach to learning costs for graph edit distance. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 389–393. IEEE (2004)

    Google Scholar 

  25. Neuhaus, M., Bunke, H.: Self-organizing maps for learning the edit costs in graph matching. IEEE Trans. Syst. Man Cybern. Part B Cybern. 35(3), 503–514 (2005)

    Article  Google Scholar 

  26. Neuhaus, M., Bunke, H.: Automatic learning of cost functions for graph edit distance. Inf. Sci. 177(1), 239–247 (2007)

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  28. Riesen, K., Bunke, H.: Iam graph database repository for graph based pattern recognition and machine learning. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) SSPR&SPR 2008. LNCS, vol. 5342, pp. 287–297. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  29. Riesen, K., Bunke, H.: Classification and Clustering of Vector Space Embedded Graphs. World Scientific, London (2010)

    Google Scholar 

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Acknowledgments

This work was supported in part by the Spanish Gov. grants TIN2012-33116, MICINN TIN2009-10435 and the UAB grant 471-01-2/2010.

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Authors and Affiliations

  1. Computer Vision Center (Universitat Autònoma de Barcelona), Edificio O, Campus UAB, Bellaterra (Cerdanyola), 089193, Barcelona, Spain

    Joan M. Núñez, Jorge Bernal, Miquel Ferrer & Fernando Vilariño

Authors
  1. Joan M. Núñez
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  2. Jorge Bernal
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  3. Miquel Ferrer
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  4. Fernando Vilariño
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Corresponding author

Correspondence to Joan M. Núñez .

Editor information

Editors and Affiliations

  1. Robarts Research Institute, London, Canada

    Xiongbiao Luo

  2. KUKA Laboratories GmbH, Augsburg, Germany

    Tobias Reichl

  3. Johns Hopkins University, Baltimore, USA

    Daniel Mirota

  4. Intuitive Surgical, Sunnyvale, California, USA

    Timothy Soper

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Núñez, J.M., Bernal, J., Ferrer, M., Vilariño, F. (2014). Impact of Keypoint Detection on Graph-Based Characterization of Blood Vessels in Colonoscopy Videos. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds) Computer-Assisted and Robotic Endoscopy. CARE 2014. Lecture Notes in Computer Science(), vol 8899. Springer, Cham. https://doi.org/10.1007/978-3-319-13410-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-13410-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13409-3

  • Online ISBN: 978-3-319-13410-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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