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

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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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title = "Characterizing non-Gaussian properties of breast images with a noisy-Laplacian distribution",

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",

author = "Abbey, {Craig K.} and Anita Nosratieh and Sheng Zhang and Eckstein, {Miguel P.} and Boone, {John M}",

year = "2011",

doi = "10.1117/12.878687",

language = "English (US)",

isbn = "9780819485083",

volume = "7966",

booktitle = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

note = "Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment ; Conference date: 16-02-2011 Through 17-02-2011",

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T1 - Characterizing non-Gaussian properties of breast images with a noisy-Laplacian distribution

AU - Abbey, Craig K.

AU - Nosratieh, Anita

AU - Zhang, Sheng

AU - Eckstein, Miguel P.

AU - Boone, John M

PY - 2011

Y1 - 2011

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.

KW - Gabor filters

KW - Image structure

KW - Non-Gaussian image statistics

KW - Scene statistics

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DO - 10.1117/12.878687

M3 - Conference contribution

AN - SCOPUS:79955866296

SN - 9780819485083

VL - 7966

BT - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

T2 - Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment

Y2 - 16 February 2011 through 17 February 2011

ER -

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

Abstract

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Keywords

ASJC Scopus subject areas

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