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Proceedings Paper

Characterizing non-Gaussian properties of breast images with a noisy-Laplacian distribution
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Paper Abstract

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.

Paper Details

Date Published: 3 March 2011
PDF: 8 pages
Proc. SPIE 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, 796611 (3 March 2011); doi: 10.1117/12.878687
Show Author Affiliations
Craig K. Abbey, Univ. of California, Santa Barbara (United States)
Univ. of California, Davis (United States)
Anita Nosratieh, Univ. of California, Davis (United States)
Sheng Zhang, Univ. of California, Santa Barbara (United States)
Miguel P. Eckstein, Univ. of California, Santa Barbara (United States)
John M. Boone, Univ. of California, Davis (United States)
UC Davis Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 7966:
Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment
David J. Manning; Craig K. Abbey, Editor(s)

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