المؤلفون: Isabelle, Martin, Klubben, William, He, Ting, Laughney, Ashley M., Glaser, Adam, Krishnaswamy, Venkataramanan, Hoopes, P. Jack, Hasan, Tayyaba, Pogue, Brian W.

المصدر: Proceedings of SPIE; Nov2011, Issue 1, p78860S-78860S-7, 7p

المؤلفون: Krishnaswamy, Venkataramanan, Laughney, Ashley M., Paulsen, Keith D., Pogue, Brian W.

المصدر: Proceedings of SPIE; Nov2011, Issue 1, p79070J-79070J-5, 5p

المؤلفون: Jiang, Shudong, Pogue, Brian W., Laughney, Ashley M., Paulsen, Keith D.

المصدر: Proceedings of SPIE; Nov2009 Part 2, Issue 1, p71740S-71740S-5, 5p

المؤلفون: Krishnaswamy, Venkataramanan, Wells, Wendy A., Laughney, Ashley M., Samkoe, Kimberley S., Pogue, Brian W.

المصدر: Proceedings of SPIE; Nov2009, Issue 1, p718710-718710-6, 6p

المؤلفون: Wax, Adam P., Backman, Vadim, Eguizabal, Alma, Laughney, Ashley M., Krishnaswamy, Venkataramanan, Wells, Wendy A., Paulsen, Keith D., Pogue, Brian W., López-Higuera, José M., Conde, Olga M.

المصدر: Proceedings of SPIE; February 2013, Vol. 8592 Issue: 1 p85920Y-85920Y-7, 773288p

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

المصدر: Proceedings of SPIE; February 2013, Vol. 8592 Issue: 1 p85920E-85920E-8, 773289p

المؤلفون: Pogue, Brian W., Gioux, Sylvain, Fernandez-Barreras, Gaspar, Real, Eusebio, Laughney, Ashley M., Krishnaswamy, Venkataramanan, Paulsen, Keith D., López-Higuera, José M., Pogue, Brian W., Conde, Olga M.

المصدر: Proceedings of SPIE; March 2015, Vol. 9311 Issue: 1 p931106-931106-6

المؤلفون: Eguizabal, Alma, Laughney, Ashley M., García Allende, Pilar Beatriz, Krishnaswamy, Venkataramanan, Wells, Wendy A., Paulsen, Keith D., Pogue, Brian W., Lopez-Higuera, Jose M., Conde, Olga M.

المصدر: Proceedings of SPIE; 2012, Vol. 8230 Issue: 1 p823014-823014-9

مستخلص: A blind separation technique based on Independent Component Analysis (ICA) is proposed for breast tumor delineation and pathologic diagnosis. Tissue morphology is determined by fitting local measures of tissue reflectance to a Mie theory approximation, parameterizing the scattering power, scattering amplitude and average scattering irradiance. ICA is applied on the scattering parameters by spatial analysis using the Fast ICA method to extract more determinant features for an accurate diagnostic. Neither training, nor comparisons with reference parameters are required. Tissue diagnosis is provided directly following ICA application to the scattering parameter images. Surgically resected breast tissues were imaged and identified by a pathologist. Three different tissue pathologies were identified in 29 samples and classified as not-malignant, malignant and adipose. Scatter plot analysis of both ICA results and optical parameters where obtained. ICA subtle ameliorates those cases where optical parameter's scatter plots were not linearly separable. Furthermore, observing the mixing matrix of the ICA, it can be decided when the optical parameters themselves are diagnostically powerful. Moreover, contrast maps provided by ICA correlate with the pathologic diagnosis. The time response of the diagnostic strategy is therefore enhanced comparing with complex classifiers, enabling near real-time assessment of pathology during breast-conserving surgery.

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

المصدر: Proceedings of SPIE; 2012, Vol. 8230 Issue: 1 p823010-823010-9

مستخلص: 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.