We present a computational method for the analysis of optical coherence tomography (OCT) images to detect
soft tissue sarcomas. The method combines the quantitative analysis of two aspects of information from the
intensity A-lines of OCT images; one is the slope of the intensity A-line with dB unit, which is determined by
the optical attenuation characteristics of tissue; the other is the standard deviation (SD) of the slope-removed
intensity A-line, which is dependent on the tissue structural features. The method is tested with pilot
experiments on ex vivo tissue samples of human fat, muscle, well differentiated liposarcoma (WDLS) and
leiomyosarcoma. Our results demonstrate the feasibility of this quantitative method in the differentiation of soft
tissue sarcomas from normal tissues. This study indicates that OCT can be a potential computer-aided means of
automatically and accurately identifying resection margins of soft tissues sarcomas during surgical treatment.
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Shang Wang ; Narendran Sudheendran ; Chih-Hao Liu ; Ravi K. Manapuram ; Valery P. Zakharov, et al.
Computational analysis of optical coherence tomography images for the detection of soft tissue sarcomas
", Proc. SPIE 8580, Dynamics and Fluctuations in Biomedical Photonics X, 85800T (February 28, 2013); doi:10.1117/12.2006638; http://dx.doi.org/10.1117/12.2006638