Characterizing non-Gaussian properties of breast images with a noisy-Laplacian distribution
Craig K. Abbey, Anita Nosratieh, Sheng Zhang, Miguel Patricio Eckstein, John M. Boone · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
It is generally well known that the appearance of breast tissue in a mammogram is considerably more complex in a statistical sense than a simple random Gaussian texture, even when the correlation structure of the Gaussian has been set to match the power-law power spectrum of mammograms. However there has not been a systematic way to characterize the extent of departure from a Gaussian process. We address this topic here by proposing a noisy-Laplacian distribution to model response histograms derived from digital (or digitized) mammograms. We describe the distribution in terms of the probability density function and cumulative density function, as well as moments up to fourth order. We also demonstrate the usefulness of the new distribution by fitting it to responses from digital mammography.