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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 2, Iss. 3 — Mar. 1, 2011
  • pp: 592–599

Rapid and accurate determination of tissue optical properties using least-squares support vector machines

Ishan Barman, Narahara Chari Dingari, Narasimhan Rajaram, James W. Tunnell, Ramachandra R. Dasari, and Michael S. Feld  »View Author Affiliations

Biomedical Optics Express, Vol. 2, Issue 3, pp. 592-599 (2011)

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Diffuse reflectance spectroscopy (DRS) has been extensively applied for the characterization of biological tissue, especially for dysplasia and cancer detection, by determination of the tissue optical properties. A major challenge in performing routine clinical diagnosis lies in the extraction of the relevant parameters, especially at high absorption levels typically observed in cancerous tissue. Here, we present a new least-squares support vector machine (LS-SVM) based regression algorithm for rapid and accurate determination of the absorption and scattering properties. Using physical tissue models, we demonstrate that the proposed method can be implemented more than two orders of magnitude faster than the state-of-the-art approaches while providing better prediction accuracy. Our results show that the proposed regression method has great potential for clinical applications including in tissue scanners for cancer margin assessment, where rapid quantification of optical properties is critical to the performance.

© 2011 OSA

OCIS Codes
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(170.6510) Medical optics and biotechnology : Spectroscopy, tissue diagnostics
(170.7050) Medical optics and biotechnology : Turbid media

ToC Category:
Spectroscopic Diagnostics

Original Manuscript: December 9, 2010
Revised Manuscript: January 27, 2011
Manuscript Accepted: February 13, 2011
Published: February 15, 2011

Ishan Barman, Narahara Chari Dingari, Narasimhan Rajaram, James W. Tunnell, Ramachandra R. Dasari, and Michael S. Feld, "Rapid and accurate determination of tissue optical properties using least-squares support vector machines," Biomed. Opt. Express 2, 592-599 (2011)

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