Advertisement

Optimal Feature Selection Applied to Multispectral Fluorescence Imaging

  • Tobias C. Wood
  • Surapa Thiemjarus
  • Kevin R. Koh
  • Daniel S. Elson
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

Recent rapid developments in multi-modal optical imaging have created a significant clinical demand for its in vivo - in situ application. This offers the potential for real-time tissue characterization, functional assessment, and intra-operative guidance. One of the key requirements for in vivo consideration is to minimise the acquisition window to avoid tissue motion and deformation, whilst making the best use of the available photons to account for correlation or redundancy between different dimensions. The purpose of this paper is to propose a feature selection framework to identify the best combination of features for discriminating between different tissue classes such that redundant or irrelevant information can be avoided during data acquisition. The method is based on a Bayesian framework for feature selection by using the receiver operating characteristic curves to determine the most pertinent data to capture. This represents a general technique that can be applied to different multi-modal imaging modalities and initial results derived from phantom and ex vivo tissue experiments demonstrate the potential clinical value of the technique.

Keywords

Feature Selection Fluorescence Imaging Multispectral Imaging BFFS Receiver Operating Characteristic 

References

  1. 1.
    DaCosta, R.S., Andersson, H., Wilson, B.C.: Molecular fluorescence excitation-emission matrices relevant to tissue spectroscopy. Photochemistry and Photobiology 78(4), 384–392 (2003)CrossRefGoogle Scholar
  2. 2.
    Schwarz, R.A., Gao, W., Daye, D., Williams, M.D., Richards-Kortum, R., Gillenwater, A.M.: Autofluorescence and diffuse reflectance spectroscopy of oral epithelial tissue using a depth-sensitive fiber-optic probe. Applied Optics 47(6), 825–834 (2008)CrossRefGoogle Scholar
  3. 3.
    Zimmermann, T., Rietdorf, J., Pepperkok, R.: Spectral imaging and its applications in live cell microscopy. FEBS Letters 546(1), 87–92 (2003)CrossRefGoogle Scholar
  4. 4.
    Berg, K., Selbo, P.K., Weyergang, A., Dietze, A., Prasmickaite, L., Bonsted, A., Engesaeter, B.O., Angell-Petersen, E., Warloe, T., Frandsen, N., Hogset, A.: Porphyrin-related photosensitizers for cancer imaging and therapeutic applications. Journal of Microscopy 218(2), 133–147 (2005)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Lekadir, K., Elson, D., Requejo-Isidro, J., Dunsby, C., McGinty, J., Galletly, N., Stamp, G., French, P., Yang, G.Z.: Tissue characterization using dimensionality reduction and fluorescence imaging. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 586–593. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Heintzelman, D.L., Utzinger, U., Fuchs, H., Zuluaga, A., Gossage, K., Gillenwater, A.M., Jacob, R., Kemp, B., Richards-Kortum, R.R.: Optimal excitation wavelengths for in vivo detection of oral neoplasia using fluorescence spectroscopy. Photochemistry and Photobiology 72(1), 103–113 (2000)CrossRefGoogle Scholar
  7. 7.
    Martin, S.F., Wood, A.D., McRobbie, M.M., Mazilu, M., McDonald, M.P., Ifor, W.D., Samuel, C., Herrington, S.: Fluorescence spectroscopy of an in vitro model of human cervical precancer identifies neoplastic phenotype. International Journal of Cancer 120(9), 1964–1970 (2007)CrossRefGoogle Scholar
  8. 8.
    Zuluaga, A.F., Utzinger, U., Durkin, A., Fuchs, H., Gillenwater, A., Jacob, R., Kemp, B., Fan, J., Richards-Kortum, R.: Fluorescence excitation emission matrices of human tissue: A system for in vivo measurement and method of data analysis. Applied Spectroscopy 53, 302–311 (1999)CrossRefGoogle Scholar
  9. 9.
    Thiemjarus, S., Lo, B.P.L., Yang, G.Z.: Feature selection for wireless sensor networks. In: First International Workshop on Wearable and Implantable Body Sensor Networks, Imperial College, London (2004)Google Scholar
  10. 10.
    Yang, G.Z., Hu, X.: Multi-sensor fusion. In: Yang, G.Z. (ed.) Body Sensor Networks, pp. 262–280. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Koh, K., Wood, T., Zhang, H., Lekadir, K., Elson, D., Yang, G.Z.: Fluorescence excitation spectroscopic imaging with a tunable light source and dimensionality reduction using fr-isomap. In: SPIE BiOS (2008)Google Scholar
  12. 12.
    Thiemjarus, S., Yang, G.Z.: An autonomic sensing framework for body sensor networks. In: Second International Conference on Body Area Networks, Florence, Italy (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tobias C. Wood
    • 1
  • Surapa Thiemjarus
    • 1
  • Kevin R. Koh
    • 1
  • Daniel S. Elson
    • 1
    • 2
  • Guang-Zhong Yang
    • 1
  1. 1.Institute of Biomedical EngineeringImperial College LondonUK
  2. 2.Department of Biosurgery and Surgical TechnologyImperial College LondonUK

Personalised recommendations