Automated algorithm for breast tissue differentiation in optical coherence tomography

Mircea Mujat; Robert Daniel Ferguson; Daniel X. Hammer; Christopher M. Gittins; Nicusor V. Iftimia

doi:10.1117/1.3156821

1 May 2009 Automated algorithm for breast tissue differentiation in optical coherence tomography

Mircea Mujat, Robert Daniel Ferguson, Daniel X. Hammer, Christopher M. Gittins, Nicusor V. Iftimia

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Journal of Biomedical Optics, Vol. 14, Issue 3, 034040 (May 2009). https://doi.org/10.1117/1.3156821

Abstract

An automated algorithm for differentiating breast tissue types based on optical coherence tomography (OCT) data is presented. Eight parameters are derived from the OCT reflectivity profiles and their means and covariance matrices are calculated for each tissue type from a training set (48 samples) selected based on histological examination. A quadratic discrimination score is then used to assess the samples from a validation set. The algorithm results for a set of 89 breast tissue samples were correlated with the histological findings, yielding specificity and sensitivity of 0.88. If further perfected to work in real time and yield even higher sensitivity and specificity, this algorithm would be a valuable tool for biopsy guidance and could significantly increase procedure reliability by reducing both the number of nondiagnostic aspirates and the number of false negatives.

©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Mircea Mujat, Robert Daniel Ferguson, Daniel X. Hammer, Christopher M. Gittins, and Nicusor V. Iftimia "Automated algorithm for breast tissue differentiation in optical coherence tomography," Journal of Biomedical Optics 14(3), 034040 (1 May 2009). https://doi.org/10.1117/1.3156821

Published: 1 May 2009

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