Frequency-based texture analysis of non-Gaussian properties of digital breast tomosynthesis images and comparison across two vendors

• Published in: Journal of medical imaging (Bellingham, Wash.) Vol. 12; no. S2; p. S22004
• Main Authors: Yang, Kai, Abbey, Craig K., Barufaldi, Bruno, Li, Xinhua, Marschall, Theodore A., Liu, Bob
• Format: Journal Article
• Language: English
• Published: United States 01.11.2025
• Subjects: image texture analysis; digital breast tomosynthesis; breast density
• ISSN: 2329-4302
• DOI: 10.1117/1.JMI.12.S2.S22004

We aim to analyze higher-order textural components of digital breast tomosynthesis (DBT) images to quantify differences in the appearance of breast parenchyma produced by different vendors. We included consecutive women who had normal screening DBT exams in January 2018 from a GE system and in adjacent years from Hologic systems. Laplacian fractional entropy (LFE), as a measure of non-Gaussian statistical properties of breast tissue texture, was calculated from for-presentation Craniocaudal (CC) view DBT slices and synthetic mammograms (SMs) through frequency-based filtering with Gabor filters, which were considered mathematical models for human visual response to image textures. The LFE values were compared within and across subjects and vendors along with secondary parameters (laterality, year-to-year, modality, and breast density) via two-way analysis of variance (ANOVA) tests using frequency as one of the two independent variables, and a -value was considered statistically significant. A total of 8529 CC view DBT slices and SM images from 73 screening exams in 25 women were analyzed. Significant differences in LFE were observed for different frequencies ( ) and across vendors (GE versus Hologic DBT: , GE versus Hologic SM: ). Significant differences in perception of breast parenchyma textures among two DBT vendors were demonstrated via higher-order non-Gaussian statistical properties. This finding extends previously observed differences in anatomical noise power spectra in DBT images and provides quantitative evidence to support caution in across-vendor comparative reading and will be beneficial to facilitate future development of vendor-neutral artificial intelligence algorithms for breast cancer screening.