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Deblurring Multispectral Laparoscopic Images

  • Geoffrey Jones
  • Neil Clancy
  • Simon Arridge
  • Dan Elson
  • Danail Stoyanov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8498)

Abstract

Multispectral imaging is an optical modality that can provide real-time in vivo information about tissue characteristics and function through signal sensitivity to chromophores in the tissue. In this paper, we present a deblurring strategy that enables imaging of dynamic tissues at wavelengths where the required acquisition time can cause significant motion blur and obscure the image. We use deconvolution for spatially varying kernels to process multispectral information obtained by using a novel laparoscopic imaging device. The trinocular design of the system allows visible light images provide information about the tissue morphology and motion that we use to construct a per pixel deformation map. We demonstrate that with the proposed method the multispectral image stack can be synthesised into a meaningful signal even in the presence of significant tissue motion. Experiments on synthetic data validate the numerical properties of the method and experiments with ex vivo tissue demonstrate the practical potential of the technique.

Keywords

Non-rigid Deblurring Multispectral Imaging Surgical Imaging Surgical Vision 

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References

  1. 1.
    Ilias, M.A., Hggblad, E., Anderson, C., Salerud, E.G.: Visible hyperspectral imaging evaluating the cutaneous response to ultraviolet radiation, pp. 644103–644103-12 (2007)Google Scholar
  2. 2.
    Sorg, B.S., Donovan, O., Cao, Y., Dewhirst, M.W., Moeller, B.J.: Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development. Journal of Biomedical Optics 10(4), 044004–044004-11 (2005)Google Scholar
  3. 3.
    Sowa, M.G., Payette, J.R., Hewko, M.D., Mantsch, H.H.: Visible-near infrared multispectral imaging of the rat dorsal skin flap. Journal of Biomedical Optics 4(4), 474–481 (1999)CrossRefGoogle Scholar
  4. 4.
    Clancy, N.T., Stoyanov, D., Sauvage, V., James, D., Yang, G.-Z., Elson, D.S.: A triple endoscope system for alignment of multispectral images of moving tissue. Biomedical Optics and 3-D Imaging, BTuD27 (2010)Google Scholar
  5. 5.
    Leitner, R., Biasio, M.D., Arnold, T., Dinh, C.V., Loog, M., Duin, R.P.: Multi-spectral video endoscopy system for the detection of cancerous tissue. Pattern Recognition Letters 34(1), 85–93 (2013)CrossRefGoogle Scholar
  6. 6.
    Stoyanov, D.: Surgical vision. Annals of Biomedical Engineering 40(2), 332–345 (2012), http://dx.doi.org/10.1007/s10439-011-0441-z CrossRefMathSciNetGoogle Scholar
  7. 7.
    Richardson, W.H.: Bayesian-based iterative method of image restoration. J. Opt. Soc. Am. 62(1), 55–59 (1972)CrossRefGoogle Scholar
  8. 8.
    Lucy, L.: An iterative technique for the rectification of observed distributions. The Astronomical Journal 79, 745 (1974)CrossRefGoogle Scholar
  9. 9.
    Dey, N., Blanc-Feraud, L., Zimmer, C., Kam, Z., Olivo-Marin, J.C., Zerubia, J.: A deconvolution method for confocal microscopy with total variation regularization. In: IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2004, vol. 2, pp. 1223–1226 (2004)Google Scholar
  10. 10.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Transactions on Graphics (TOG) 25(3), 787–794 (2006)CrossRefGoogle Scholar
  11. 11.
    Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 233–240. IEEE (2011)Google Scholar
  12. 12.
    Gregson, J., Heide, F., Hullin, M.B., Rouf, M., Heidrich, W.: Stochastic Deconvolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2013) (to appear)Google Scholar
  13. 13.
    Li, F., Yu, J., Chai, J.: A hybrid camera for motion deblurring and depth map super-resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  14. 14.
    Tai, Y.-W., Du, H., Brown, M.S., Lin, S.: Correction of spatially varying image and video motion blur using a hybrid camera. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(6), 1012–1028 (2010)CrossRefGoogle Scholar
  15. 15.
    Clancy, N.T., Stoyanov, D., James, D.R.C., Marco, A.D., Sauvage, V., Clark, J., Yang, G.Z., Elson, D.S.: Multispectral image alignment using a three channel endoscope in vivo during minimally invasive surgery. Biomed. Opt. Express 3(10), 2567–2578 (2012)CrossRefGoogle Scholar
  16. 16.
    Holmes, T.J.: Blind deconvolution of quantum-limited incoherent imagery: maximum-likelihood approach. J. Opt. Soc. Am. A 9(7), 1052–1061 (1992)CrossRefGoogle Scholar
  17. 17.
    Fish, D., Brinicombe, A., Pike, E., Walker, J.: Blind deconvolution by means of the richardson–lucy algorithm. JOSA A 12(1), 58–65 (1995)CrossRefGoogle Scholar
  18. 18.
    Ben-Ezra, M., Nayar, S.K.: Motion deblurring using hybrid imaging. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1–657. IEEE (2003)Google Scholar
  19. 19.
    Tai, Y., Tan, P., Brown, M.: Richardson-lucy deblurring for scenes under a projective motion path. IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)Google Scholar
  20. 20.
    Prahl, S.A.: Tabulated molar extinction coefficient for hemoglobin in waterGoogle Scholar
  21. 21.
    Yuan, L., Sun, J., Quan, L., Shum, H.-Y.: Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graph. 27(3), 74:1–74:10 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Geoffrey Jones
    • 1
  • Neil Clancy
    • 2
    • 3
  • Simon Arridge
    • 1
  • Dan Elson
    • 2
    • 3
  • Danail Stoyanov
    • 1
  1. 1.Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonUK
  2. 2.Hamlyn Centre for Robotic Surgery, Institute of Global Health InnovationImperial College LondonUK
  3. 3.Department of Surgery and CancerImperial College LondonUK

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