Prototype System for Semantic Retrieval of Neurological PET Images

  • Stephen Batty
  • John Clark
  • Tim Fryer
  • Xiaohong Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4987)

Abstract

Positron Emission Tomography (PET) is used within neurology to study the underlying biochemical basis of cognitive functioning. Due to the inherent lack of anatomical information its study in conjunction with image retrieval is limited. Content based image retrieval (CBIR) relies on visual features to quantify and classify images with a degree of domain specific saliency. Numerous CBIR systems have been developed semantic retrieval, has however not been performed. This paper gives a detailed account of the framework of visual features and semantic information utilized within a prototype image retrieval system, for PET neurological data. Images from patients diagnosed with different and known forms of Dementia are studied and compared to controls. Image characteristics with medical saliency are isolated in a top down manner, from the needs of the clinician - to the explicit visual content. These features are represented via Gabor wavelets and mean activity levels of specific anatomical regions. Preliminary results demonstrate that these representations are effective in reflecting image characteristics and subject diagnosis; consequently they are efficient indices within a semantic retrieval system.

Keywords

PET neurological content based image retrieval dementia semantic retrieval 

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References

  1. 1.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Machine Intel. 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  2. 2.
    Muller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. Int. J. Med. Inform. 73(1), 1–23 (2004)CrossRefGoogle Scholar
  3. 3.
    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. Comput. Vis. Image Understand 75(1–2), 111–132 (1999)CrossRefGoogle Scholar
  4. 4.
    Liu, Y., Dellaert, F.: Classification-driven medical image retrieval. In: Proceedings of the ARPA Image Understanding Workshop (1997)Google Scholar
  5. 5.
    Rahman, M.M., Bhattacharya, P., Desai, B.C.: A Framework for Medical Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques With Relevance Feedback. IEEE transactions on Information Technology in Biomedicine 11(1), 58–69 (2007)CrossRefGoogle Scholar
  6. 6.
    Cai, W., Feng, D.D., Fulton, R.: Content-based retrieval of dynamic PET functional images. IEEE Trans. Information Technol. Biomed. 4(2), 152–158 (2000)CrossRefGoogle Scholar
  7. 7.
    Montreal Neurological Institute, http://www.bic.mni.mcgill.ca
  8. 8.
    Talairarch, J., Tournoux, P.: Co-planar stereotaxic atlas of the human brain. Thieme. New York (1988)Google Scholar
  9. 9.
    Lancaster, J.L., Woldorff, M.G., Parsons, L.M., Liotti, M., Freitas, C.S., Rainey, L., Kochunov, P.V., Nickerson, D., Mikiten, S.A., Fox, P.T.: Automated Talairach Atlas labels for functional brain mapping. HBM 10, 120–131 (2000)CrossRefGoogle Scholar
  10. 10.
    Friston, K.J., Ashburner, J., Poline, J.B., Frith, C.D., Heather, J.D., Frackowiak, R.S.J.: Spatial Registration and Normalization of Images. Human Brain Mapping 2, 165–189 (1995)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Nestor, P.J., Fryer, T.D., Ikeda, M., Hodges, J.R.: Retrosplenial cortex (BA 29/30) hypometabolism in mild cognitive impairment (prodromal Alzheimer’s disease). European Journal of Neuroscience 18(9), 2663 (2003)CrossRefGoogle Scholar
  13. 13.
    Duvernoy, H.M.: The Human Brain: Surface, three dimensional sectional anatomy with MRI, and blood supply. Springer, New YorkGoogle Scholar
  14. 14.
    Smith, J.R., Chang, S.: Automated Image Retrieval Using Color and Texture, Columbia, University Technical Report TR# 414-95-20 (July 1995)Google Scholar
  15. 15.
    Ma, W.Y., Manjunath, B.S.: Texture Features for Browsing and Retrieval of Image Data. IEEE transactions on Pattern Analysis and Machine Intelligence 18(8) (1996)Google Scholar
  16. 16.
    Friston, K.J., Frith, C.D., Liddle, P.F., Dolan, R.J., Lammertsma, A.A., Frackowiak, R.S.: The relationship between global and local changes in PET scans. J. Cereb. Blood Flow Metab. 10(4), 458–466 (1990)Google Scholar
  17. 17.
    Scarmeas, N., Habeck, C.G., Zarahn, E., Anderson, K.E., Park, A., Hilton, J., Pelton, G.H., Tabert, M.H., Honig, L.S., Moeller, J.R., Devanand, D.P., Sterna, Y.: Covariance PET patterns in early Alzheimer’s disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance. NeuroImage 23, 35–45 (2004)CrossRefGoogle Scholar
  18. 18.
    Güld, M.O., Kohnen, M., Keysers, D., Schubert, H., Wein, B.B., Bredno, J., Lehmann, T.M.: Quality of DICOM header information for image categorization. In: Proceedings of the International Symposium on Medical Imaging, vol. 4685, pp. 280–287 (2002)Google Scholar
  19. 19.
    Rapoport, M., Reekum, R., Mayberg, H.: A Selective Review: The Role of the Cerebellum in Cognition and Behavior. J. Neuropsychiatry Clin Neurosci. 12, 193–198 (2000)Google Scholar
  20. 20.
    Batty, S., Gao, X.W., Clark, J., Fryer, T.: Content-based Retrieval of PET images via Localised Anatomical texture measurements and mean activity levels. In: Proceedings of International Conference on Medical Imaging and Telemedicine, pp. 70–74 (2005) ISBN:1-85924-252-9Google Scholar
  21. 21.
    Burns, M., Leung, K., Rowland, A., Vickers, J., Hajnal, J.V., Rueckert, D., Hill, D.L.G.: Information eXtraction from Images (IXI) - Grid Services for Medical Imaging. In: DiDaMIC 2004, Rennes, France (2004)Google Scholar
  22. 22.
    US patent number 7,158,961; Methods and apparatus for estimating similarity. Assigned to Google Inc. (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Stephen Batty
    • 1
  • John Clark
    • 2
  • Tim Fryer
    • 2
  • Xiaohong Gao
    • 1
  1. 1.The Burroughs, HendonMiddlsex UniversityLondonUK
  2. 2.WBIC, Addenbrookes HospitalCambridge UniversityUK

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