Advertisement

Similarity Retrieval of Angiogram Images BASED on a Flexible Shape Model

  • Tanveer Syeda-Mahmood
  • Colin B. Compas
  • David Beymer
  • Ritwik Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)

Abstract

In this paper we address the problem of finding similar coronary angiograms from a database of angiograms using a new constrained nonrigid shape model for the description of coronary arteries. The model captures the non-rigid variations in the artery shapes while still preserving the overall perceptual spatial layout based on the articulation constraints between arteries. Shape matching involves testing for class membership using the constraints specified in the model. The shape similarity method is demonstrated in a similarity retrieval application on a large database of angiogram images.

Keywords

Right Coronary Artery Dynamic Time Warp Longe Common Subsequence Longe Common Subsequence Coronary Artery Image 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Medis medical imaging systems, Inc., http://www.medis.nl/index.htm
  2. 2.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI 24, 509–522 (2002)CrossRefGoogle Scholar
  3. 3.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research, 725–760 (2007)Google Scholar
  5. 5.
    Haris, K., Efstratiadis, S., Maglaveras, N., Pappas, C., Gourassas, J., Louridas, G.: Model-based morphological segmentation and labeling of coronary angiograms. IEEE-TMI 18(10), 1003–1015 (1999)Google Scholar
  6. 6.
    Perfetti, R., Ricci, E., Casali, D., Costantini, G.: A cnn based algorithm for retinal vessel segmentation. In: ICC 2008: Proceedings of the 12th WSEAS International Conference on Circuits, pp. 152–157 (2008)Google Scholar
  7. 7.
    Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. IEEE Medical Image Analysis, 143–168 (1998)Google Scholar
  8. 8.
    Syeda-Mahmood, T., Beymer, D., Wang, F.: Shape-based matching of ECG recordings. IEEE EMBC, 2012–2018 (2007)Google Scholar
  9. 9.
    Syeda-Mahmood, T., Beymer, D., Wang, F., Mahmood, A., Lundstrom, R., Shafee, N., Holve, T.: Automatic selection of keyframes from angiogram videos. In: ICPR, pp. 4008–4011 (2010)Google Scholar
  10. 10.
    Syeda-Mahmood, T., Turaga, P., Beymer, D., Wang, F., Amir, A., Greenspan, H., Pohl, K.: Shape-based similarity retrieval of doppler images for clinical decision support. In: IEEE CVPR, pp. 855–862 (2010)Google Scholar
  11. 11.
    Cormen, T., et al.: Introduction to algorithms, p. 1985. MIT Press (1985)Google Scholar
  12. 12.
    Yeh, M., Cheng, K.T.: A string matching approach for visual retrieval and classification. In: ACM-MIR (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tanveer Syeda-Mahmood
    • 1
  • Colin B. Compas
    • 1
  • David Beymer
    • 1
  • Ritwik Kumar
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA

Personalised recommendations