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Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition

  • Geoffrey JonesEmail author
  • Neil T. Clancy
  • Xiaofei Du
  • Maria Robu
  • Simon Arridge
  • Daniel S. Elson
  • Danail Stoyanov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Tissue oxygenation and perfusion can be an indicator for organ viability during minimally invasive surgery, for example allowing real-time assessment of tissue perfusion and oxygen saturation. Multispectral imaging is an optical modality that can inspect tissue perfusion in wide field images without contact. In this paper, we present a novel, fast method for using RGB images for MSI, which while limiting the spectral resolution of the modality allows normal laparoscopic systems to be used. We exploit the discrete Haar decomposition to separate individual video frames into low pass and directional coefficients and we utilise a different multispectral estimation technique on each. The increase in speed is achieved by using fast Tikhonov regularisation on the directional coefficients and more accurate Bayesian estimation on the low pass component. The pipeline is implemented using a graphics processing unit (GPU) architecture and achieves a frame rate of approximately 15 Hz. We validate the method on animal models and on human data captured using a da Vinci stereo laparoscope.

Keywords

Minimal invasive surgery Intraoperative imaging Multispectral imaging 

Notes

Acknowledgements

This work was supported by the EPSRC (EP/N013220/1, EP/N022750/1, EP/N027078/1, NS/A000027/1, EP/P012841/1), The Wellcome Trust (WT101957, 201080/Z/16/Z) and the EU-Horizon2020 project EndoVESPA (H2020-ICT-2015-688592).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Geoffrey Jones
    • 1
    Email author
  • Neil T. Clancy
    • 1
  • Xiaofei Du
    • 1
  • Maria Robu
    • 1
  • Simon Arridge
    • 1
  • Daniel S. Elson
    • 1
  • Danail Stoyanov
    • 1
  1. 1.The Centre for Medical Image ComputingUniversity College LondonLondonUK

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