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

Craig K. Abbey; Anita Nosratieh; Sheng Zhang; Miguel P. Eckstein; John M Boone

doi:10.1117/12.878687

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

Craig K. Abbey, Anita Nosratieh, Sheng Zhang, Miguel P. Eckstein, John M Boone

Radiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	7966
DOIs	https://doi.org/10.1117/12.878687
State	Published - 2011
Event	Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment - Lake Buena Vista, FL, United States Duration: Feb 16 2011 → Feb 17 2011

Other

Other	Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment
Country	United States
City	Lake Buena Vista, FL
Period	2/16/11 → 2/17/11

Keywords

Gabor filters
Image structure
Non-Gaussian image statistics
Scene statistics

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Electronic, Optical and Magnetic Materials
Biomaterials
Radiology Nuclear Medicine and imaging

Access to Document

10.1117/12.878687

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Cite this

Abbey, C. K., Nosratieh, A., Zhang, S., Eckstein, M. P., & Boone, J. M. (2011). Characterizing non-Gaussian properties of breast images with a noisy-Laplacian distribution. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 7966). [796611] https://doi.org/10.1117/12.878687

Abbey, CK, Nosratieh, A, Zhang, S, Eckstein, MP & Boone, JM 2011, Characterizing non-Gaussian properties of breast images with a noisy-Laplacian distribution. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 7966, 796611, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment, Lake Buena Vista, FL, United States, 2/16/11. https://doi.org/10.1117/12.878687

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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.",

keywords = "Gabor filters, Image structure, Non-Gaussian image statistics, Scene statistics",

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AU - Abbey, Craig K.

AU - Nosratieh, Anita

AU - Zhang, Sheng

AU - Eckstein, Miguel P.

AU - Boone, John M

PY - 2011

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N2 - 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.

AB - 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.

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KW - Image structure

KW - Non-Gaussian image statistics

KW - Scene statistics

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