Fractal analysis of scatter imaging signatures to distinguish breast pathologies

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

doi:10.1117/12.2003830

21 February 2013 Fractal analysis of scatter imaging signatures to distinguish breast pathologies

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

Author Affiliations +

Proceedings Volume 8592, Biomedical Applications of Light Scattering VII; 85920Y (2013) https://doi.org/10.1117/12.2003830
Event: SPIE BiOS, 2013, San Francisco, California, United States

Abstract

Fractal analysis combined with a label-free scattering technique is proposed for describing the pathological architecture of tumors. Clinicians and pathologists are conventionally trained to classify abnormal features such as structural irregularities or high indices of mitosis. The potential of fractal analysis lies in the fact of being a morphometric measure of the irregular structures providing a measure of the object’s complexity and self-similarity. As cancer is characterized by disorder and irregularity in tissues, this measure could be related to tumor growth. Fractal analysis has been probed in the understanding of the tumor vasculature network. This work addresses the feasibility of applying fractal analysis to the scattering power map (as a physical modeling) and principal components (as a statistical modeling) provided by a localized reflectance spectroscopic system. Disorder, irregularity and cell size variation in tissue samples is translated into the scattering power and principal components magnitude and its fractal dimension is correlated with the pathologist assessment of the samples. The fractal dimension is computed applying the box-counting technique. Results show that fractal analysis of ex-vivo fresh tissue samples exhibits separated ranges of fractal dimension that could help classifier combining the fractal results with other morphological features. This contrast trend would help in the discrimination of tissues in the intraoperative context and may serve as a useful adjunct to surgeons.

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Alma Eguizabal, Ashley M. Laughney, Venkataramanan Krishnaswamy, Wendy A. Wells, Keith D. Paulsen, Brian W. Pogue, José M. López-Higuera, and Olga M. Conde "Fractal analysis of scatter imaging signatures to distinguish breast pathologies", Proc. SPIE 8592, Biomedical Applications of Light Scattering VII, 85920Y (21 February 2013); https://doi.org/10.1117/12.2003830

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