Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density

Craig K. Abbey; Margarita L. Zuley; Jonathan D. Victor

doi:10.1117/1.JMI.10.6.065502

11 November 2023 Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density

Craig K. Abbey, Margarita L. Zuley, Jonathan D. Victor

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 6, 065502 (November 2023). https://doi.org/10.1117/1.JMI.10.6.065502

Abstract

Purpose

Anatomical “noise” is an important limitation of full-field digital mammography. Understanding its impact on clinical judgments is made difficult by the complexity of breast parenchyma, which results in image texture not fully captured by the power spectrum. While the number of possible parameters for characterizing anatomical noise is quite large, a specific set of local texture statistics has been shown to be visually salient, and human sensitivity to these statistics corresponds to their informativeness in natural scenes.

Approach

We evaluate these local texture statistics in addition to standard power-spectral measures to determine whether they have additional explanatory value for radiologists’ breast density judgments. We analyzed an image database consisting of 111 disease-free mammographic screening exams (4 views each) acquired at the University of Pittsburgh Medical Center. Each exam had a breast density score assigned by the examining radiologist. Power-spectral descriptors and local image statistics were extracted from images of breast parenchyma. Model-selection criteria and accuracy were used to assess the explanatory and predictive value of local image statistics for breast density judgments.

Results

The model selection criteria show that adding local texture statistics to descriptors of the power spectra produce better explanatory and predictive models of radiologists’ judgments of breast density. Thus, local texture statistics capture, in some form, non-Gaussian aspects of texture that radiologists are using.

Conclusions

Since these local texture statistics are expected to be impacted by imaging factors like modality, dose, and image processing, they suggest avenues for understanding and optimizing observer performance.

Citation Download Citation

Craig K. Abbey, Margarita L. Zuley, and Jonathan D. Victor "Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density," Journal of Medical Imaging 10(6), 065502 (11 November 2023). https://doi.org/10.1117/1.JMI.10.6.065502

Received: 23 May 2023; Accepted: 16 October 2023; Published: 11 November 2023

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $24.00

Non-members: $28.00 ADD TO CART

JOURNAL ARTICLE
16 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Statistical modeling

Breast density

Mammography

Medical statistics

Image segmentation

Atomic force microscopy

Breast

Purpose

Approach

Results

Conclusions

Show All Keywords

Keywords/Phrases

Search In:

Publication Years