Advertisement

A Differential Method for Representing Spinal MRI for Perceptual-CBIR

  • Marcelo Ponciano-Silva
  • Pedro H. Bugatti
  • Rafael M. Reis
  • Paulo M. Azevedo-Marques
  • Marcello H. Nogueira-Barbosa
  • Caetano TrainaJr.
  • Agma Juci Machado Traina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Image exams are a fundamental tool in health care for decision making. A challenge in Content-based image retrieval (CBIR) is to provide a timely answer that complies with the specialist’s expectation. There are different systems with different techniques to CBIR in literature. However, even with so much research, there are still particular challenges to be overcame, such as the semantic gap. In this paper, we presented a new spinal-image comparison method based on the perception of specialists during his/her analysis of spine lesions. We take advantage of a color extractor and propose a shape descriptor considering the visual patterns that the radiologists use to recognize anomalies in images. The experiments shown that our approach achieved promising results, testifying that the automatic comparison of images should consider all relevant visual aspects and comparisons’ criteria, which are defined by the specialists.

Keywords

Content-Based Medical Image Retrieval Features Extraction Spinal Images 

References

  1. 1.
    Antani, S., Lee, D.J., Long, L.R., Thoma, G.R.: Evaluation of shape similarity measurement methods for spine X-ray images. J. Visual Communication and Image Representation 15(3), 285–302 (2004)CrossRefGoogle Scholar
  2. 2.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval - the concepts and technology behind search, 2nd edn. Pearson Education Ltd., Harlow (2011)Google Scholar
  3. 3.
    Deserno, T.M., Antani, A., Long, L.R.: Ontology of Gaps in Content-Based Image Retrieval. J. Digital Imaging 22(2), 202–215 (2009)CrossRefGoogle Scholar
  4. 4.
    Genant, H.K., Wu, C.Y., van Kuijk, C., Nevitt, M.C.: Vertebral fracture assessment using a semiquantitative technique. J. of Bone and Mineral Research: The Official J. of the American Society for Bone and Mineral Research 8(9), 1137–1148 (1993)CrossRefGoogle Scholar
  5. 5.
    Güld, M.O., Thies, C., Fischer, B., Lehmann, T.M.: A generic concept for the implementation of medical image retrieval systems. I. J. Medical Informatics 76(2-3), 252–259 (2007)CrossRefGoogle Scholar
  6. 6.
    Hsu, W., Antani, S., Long, L.R., Neve, L., Thoma, G.R.: SPIRS: A Web-based image retrieval system for large biomedical databases. I. J. Medical Informatics 78 (S1) S13–S24 (2009)Google Scholar
  7. 7.
    Huang, H.K.: PACS and Imaging Informatics: Basic Principles and Applications, 2nd edn. Wiley-Blackwell, Hoboken (2010)Google Scholar
  8. 8.
    Khotanzad, A., Hong, Y.H.: Invariant Image Recognition by Zernike Moments. IEEE Trans. Pattern Anal. Mach. Intell. 12(5), 489–497 (1990)CrossRefGoogle Scholar
  9. 9.
    Kumaran, N., Bhavani, R.: Spine MRI Image Retrieval using Texture Features. I. J. of Computer Applications 46(24), 1–7 (2012)Google Scholar
  10. 10.
    Medina, J.M., Jaime-Castillo, S., Jiménez, E.: A DICOM viewer with flexible image retrieval to support diagnosis and treatment of scoliosis. Expert Syst. Appl. 39(10), 8799–8808 (2012)CrossRefGoogle Scholar
  11. 11.
    Ponciano-Silva, M., Traina, A.J.M., Azevedo-Marques, P.M., Felipe, J.C., Traina, C.J.: Including the perceptual parameter to tune the retrieval ability of pulmonary cbir systems. In: CBMS, pp. 8–17 (2009)Google Scholar
  12. 12.
    Shyu, C.R., Brodley, C.E., Kak, A.C., Kosaka, A., Aisen, A.M., Broderick, L.S.: ASSERT: A Physician-in-the-Loop Content-Based Retrieval System for HRCT Image Databases. Comp. Vision and Image Understanding 75(1-2), 111–132 (1999)CrossRefGoogle Scholar
  13. 13.
    Stehling, R.O., Nascimento, M.A., Falcão, A.X.: A compact and efficient image retrieval approach based on border/interior pixel classification. In: CIKM, pp. 102–109 (2002)Google Scholar
  14. 14.
    Town, C.: Content-Based and Similarity-Based Querying for Broad-Usage Medical Image Retrieval. In: Sidhu, A.S., Dhillon, S.K. (eds.) Advances in Biomedical Infrastructure 2013. SCI, vol. 477, pp. 63–76. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Wang, K.C., Jeanmenne, A., Weber, G.M., Thawait, S.K., Carrino, J.A.: An Online Evidence-Based Decision Support System for Distinguishing Benign from Malignant Vertebral Compression Fractures by Magnetic Resonance Imaging Feature Analysis. J. Digital Imaging 24(3), 507–515 (2011)CrossRefGoogle Scholar
  16. 16.
    Xue, Z., Long, L.R., Antani, S., Thoma, G.R.: Spine X-ray image retrieval using partial vertebral boundaries. In: CBMS, pp. 1–6 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marcelo Ponciano-Silva
    • 1
    • 2
  • Pedro H. Bugatti
    • 3
  • Rafael M. Reis
    • 4
  • Paulo M. Azevedo-Marques
    • 4
  • Marcello H. Nogueira-Barbosa
    • 4
  • Caetano TrainaJr.
    • 1
  • Agma Juci Machado Traina
    • 1
  1. 1.Department of Computer ScienceUniversity of São PauloSão CarlosBrazil
  2. 2.Fed. Inst. of Education, Science and Technology of the Triângulo MineiroBrazil
  3. 3.Dept. of Computer EngineeringFed. Tech. University of ParanáBrazil
  4. 4.School of Medicine of University of São PauloRibeirão PretoBrazil

Personalised recommendations