Abstract: A spectral analysis technique to enhance tumor contrast during breast conserving surgery is proposed. A set of 29 surgically-excised breast tissues have been imaged in local reflectance geometry. Measures of broadband reflectance are directly analyzed using Principle Component Analysis (PCA), on a per sample basis, to extract areas of maximal spectral variation. A dynamic selection threshold has been applied to obtain the final number of principal components, accounting for inter-patient variability. A blind separation technique based on Independent Component Analysis (ICA) is then applied to extract diagnostically powerful results. ICA application reveals that the behavior of one independent component highly correlates with the pathologic diagnosis and it surpasses the contrast obtained using empirical models. Moreover, blind detection characteristics (no training, no comparisons with training reference data) and no need for parameterization makes the automated diagnosis simple and time efficient, favoring its translation to the clinical practice. Correlation coefficient with model-based results up to 0.91 has been achieved.

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