@conference{10902/2514,
year = {2013},
month = {2},
url = {http://hdl.handle.net/10902/2514},
abstract = {Texture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of notmalignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H and E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.},
organization = {This work has been supported by CYCIT projects DA2TOI (FIS2010-19860) and TFS (TEC2010-20224-C02-02), as well as FPU PhD Scholarship (FPU12/04130) funded by the Spanish Government.},
publisher = {SPIE Society of Photo-Optical Instrumentation Engineers},
publisher = {Proceedings of SPIE, 2013, vol. 8592, 85920E},
publisher = {Biomedical Applications of Light Scattering VII, San Francisco (CA), 2013},
title = {Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction},
author = {Eguizabal Aguado, Alma and Laughney, Ashley M. and García Allende, Pilar Beatriz and Krishnaswamy, Venkataramanan and Wells, Wendy A. and Paulsen, Keith D. and Pogue, Brian William and López Higuera, José Miguel and Conde Portilla, Olga María},
}