Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

Alma Eguizabal; Ashley M. Laughney; Pilar Beatriz Garcia Allende; Venkataramanan Krishnaswamy; Wendy A. Wells; Keith D. Paulsen; Brian W. Pogue; José M. López-Higuera; Olga M. Conde

doi:10.1117/12.2003814

21 February 2013 Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

Alma Eguizabal, Ashley M. Laughney, Pilar Beatriz Garcia Allende, Venkataramanan Krishnaswamy, Wendy A. Wells, Keith D. Paulsen, Brian W. Pogue, José M. López-Higuera, Olga M. Conde

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Proceedings Volume 8592, Biomedical Applications of Light Scattering VII; 85920E (2013) https://doi.org/10.1117/12.2003814
Event: SPIE BiOS, 2013, San Francisco, California, United States

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.

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Alma Eguizabal, Ashley M. Laughney, Pilar Beatriz Garcia Allende, Venkataramanan Krishnaswamy, Wendy A. Wells, Keith D. Paulsen, Brian W. Pogue, José M. López-Higuera, and Olga M. Conde "Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction", Proc. SPIE 8592, Biomedical Applications of Light Scattering VII, 85920E (21 February 2013); https://doi.org/10.1117/12.2003814

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KEYWORDS

Tissues

Scattering

Tissue optics

Diagnostics

Breast cancer

Light scattering

Statistical analysis

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