Content-Based Retrieval in Endomicroscopy: Toward an Efficient Smart Atlas for Clinical Diagnosis

  • Barbara André
  • Tom Vercauteren
  • Nicholas Ayache
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7075)

Abstract

In this paper we present the first Content-Based Image Retrieval (CBIR) framework in the field of in vivo endomicroscopy, with applications ranging from training support to diagnosis support. We propose to adjust the standard Bag-of-Visual-Words method for the retrieval of endomicroscopic videos. Retrieval performance is evaluated both indirectly from a classification point-of-view, and directly with respect to a perceived similarity ground truth. The proposed method significantly outperforms, on two different endomicroscopy databases, several state-of-the-art methods in CBIR. With the aim of building a self-training simulator, we use retrieval results to estimate the interpretation difficulty experienced by the endoscopists. Finally, by incorporating clinical knowledge about perceived similarity and endomicroscopy semantics, we are able: 1) to learn an adequate visual similarity distance and 2) to build visual-word-based semantic signatures that extract, from low-level visual features, a higher-level clinical knowledge expressed in the endoscopist own language.

Keywords

Visual Word Scale Invariant Feature Transform Retrieval Performance Colonic Polyp Semantic Concept 
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.
    Sivic, J., Zisserman, A.: Efficient visual search of videos cast as text retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(4), 591–606 (2009)CrossRefGoogle Scholar
  2. 2.
    Zhang, J., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. International Journal of Computer Vision 73, 213–238 (2007)CrossRefGoogle Scholar
  3. 3.
    Syeda-Mahmood, T.F., Wang, F., Beymer, D.: Recognition of object categories using affine kernels. In: Multimedia Information Retrieval, pp. 15–24 (2010)Google Scholar
  4. 4.
    André, B., Vercauteren, T., Buchner, A.M., Wallace, M.B., Ayache, N.: A smart atlas for endomicroscopy using automated video retrieval. Medical Image Analysis 15(4), 460–476 (2011)CrossRefGoogle Scholar
  5. 5.
    Vercauteren, T., Perchant, A., Malandain, G., Pennec, X., Ayache, N.: Robust mosaicing with correction of motion distortions and tissue deformation for in vivo fibered microscopy. Medical Image Analysis 10(5), 673–692 (2006)CrossRefGoogle Scholar
  6. 6.
    Müller, H., Kalpathy-Cramer, J., Eggel, I., Bedrick, S., Reisetter, J., Kahn, C.E., Hersh, W.R.: Overview of the clef 2010 medical image retrieval track. In: CLEF (Notebook Papers/LABs/Workshops) (2010)Google Scholar
  7. 7.
    Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: Current status and future directions. Journal of Digital Imaging 24(2), 208–222 (2011)CrossRefGoogle Scholar
  8. 8.
    Haralick, R.M.: Statistical and structural approaches to texture. Proceedings of the IEEE 67, 786–804 (1979)CrossRefGoogle Scholar
  9. 9.
    Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision 43, 29–44 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8 (2008)Google Scholar
  11. 11.
    André, B., Vercauteren, T., Buchner, A.M., Wallace, M.B., Ayache, N.: Retrieval Evaluation and Distance Learning from Perceived Similarity between Endomicroscopy Videos. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 297–304. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    André, B., Vercauteren, T., Buchner, A.M., Shahid, M.W., Wallace, M.B., Ayache, N.: An Image Retrieval Approach to Setup Difficulty Levels in Training Systems for Endomicroscopy Diagnosis. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 480–487. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Kiesslich, R., Burg, J., Vieth, M., Gnaendiger, J., Enders, M., Delaney, P., Polglase, A., McLaren, W., Janell, D., Thomas, S., Nafe, B., Galle, P.R., Neurath, M.F.: Confocal laser endoscopy for diagnosing intraepithelial neoplasias and colorectal cancer in vivo. Gastroenterology 127(3), 706–713 (2004)CrossRefGoogle Scholar
  14. 14.
    Rasiwasia, N., Moreno, P.J., Vasconcelos, N.: Bridging the gap: Query by semantic example. IEEE Transactions on Multimedia 9(5), 923–938 (2007)CrossRefGoogle Scholar
  15. 15.
    Kwitt, R., Rasiwasia, N., Vasconcelos, N., Uhl, A., Häfner, M., Wrba, F.: Learning Pit Pattern Concepts for Gastroenterological Training. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 280–287. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    André, B., Vercauteren, T., Buchner, A.M., Wallace, M.B., Ayache, N.: Learning semantic and visual similarity for endomicroscopy video retrieval. INRIA Technical Report RR-7722, INRIA (August 2011)Google Scholar
  17. 17.
    Philbin, J., Isard, M., Sivic, J., Zisserman, A.: Descriptor learning for efficient retrieval. In: Daniilidis, K. (ed.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 677–691. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Barbara André
    • 1
    • 2
  • Tom Vercauteren
    • 1
  • Nicholas Ayache
    • 2
  1. 1.Mauna Kea Technologies (MKT)ParisFrance
  2. 2.INRIA - Asclepios Research ProjectSophia AntipolisFrance

Personalised recommendations