Research Papers

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

[+] Author Affiliations
Pilar Beatriz Garcia-Allende

University of Cantabria, Photonics Engineering Group, Avda. de los Castros S/N, Santander 39005, Spain

Venkataramanan Krishnaswamy

Dartmouth College, Thayer School of Engineering, 8000 Cummings Hall, Hanover, New Hampshire 03755

P. Jack Hoopes

Dartmouth College, Thayer School of Engineering, 8000 Cummings Hall, Hanover, New Hampshire 03755 and Dartmouth Medical School, Department of Surgery, 1 Medical Center Drive, Lebanon, New Hampshire 03755

Kimberley S. Samkoe

Dartmouth College, Thayer School of Engineering, 8000 Cummings Hall, Hanover, New Hampshire 03755

Olga M. Conde

University of Cantabria, Photonics Engineering Group, Avda. de los Castros S/N, Santander 39005, Spain

Brian W. Pogue

Dartmouth College, Thayer School of Engineering, 8000 Cummings Hall, Hanover, New Hampshire 03755 and Dartmouth Medical School, Department of Surgery, 1 Medical Center Drive, Lebanon, New Hampshire 03755

J. Biomed. Opt. 14(3), 034034 (June 18, 2009). doi:10.1117/1.3155512
History: Received October 31, 2008; Revised March 13, 2009; Accepted April 20, 2009; Published June 18, 2009
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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.

© 2009 Society of Photo-Optical Instrumentation Engineers

Topics

Scattering ; Tissues

Citation

Pilar Beatriz Garcia-Allende ; Venkataramanan Krishnaswamy ; P. Jack Hoopes ; Kimberley S. Samkoe ; Olga M. Conde, et al.
"Automated identification of tumor microscopic morphology based on macroscopically measured scatter signatures", J. Biomed. Opt. 14(3), 034034 (June 18, 2009). ; http://dx.doi.org/10.1117/1.3155512


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