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Journal of Biomedical Optics

Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures
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Paper Abstract

An automated algorithm and methodology is presented to identify tumor-tissue morphologies based on broadband scatter data measured by raster scan imaging of the samples. A quasi-confocal reflectance imaging system was used to directly measure the tissue scatter reflectance in situ, and the spectrum was used to identify the scattering power, amplitude, and total wavelength-integrated intensity. Pancreatic tumor and normal samples were characterized using the instrument, and subtle changes in the scatter signal were encountered within regions of each sample. Discrimination between normal versus tumor tissue was readily performed using a K-nearest neighbor classifier algorithm. A similar approach worked for regions of tumor morphology when statistical preprocessing of the scattering parameters was included to create additional data features. This type of automated interpretation methodology can provide a tool for guiding surgical resection in areas where microscopy imaging cannot be realized efficiently by the surgeon. In addition, the results indicate important design changes for future systems.

Paper Details

Date Published: 1 May 2009
PDF: 13 pages
J. Biomed. Opt. 14(3) 034034 doi: 10.1117/1.3155512
Published in: Journal of Biomedical Optics Volume 14, Issue 3
Show Author Affiliations
Pilar Beatriz Garcia-Allende, Univ. de Cantabria (Spain)
Venkat Krishnaswamy, Dartmouth College (United States)
P. Jack Hoopes, Dartmouth Hitchcock Medical Ctr. (United States)
Kimberley S. Samkoe, Dartmouth College (United States)
Olga M. Conde, Univ. de Cantabria (Spain)
Brian W. Pogue, Dartmouth College (United States)

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