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Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment

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Abstract

This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board–approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density–based features. We trained and developed two different global mammographic image feature analysis–based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature–based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods.

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Abbreviations

DBT:

Digital breast tomosynthesis

FFDM:

Full-field digital mammography

CC:

Craniocaudal

MLO:

Mediolateral oblique

2D:

Two-dimensional

2DSM:

2D synthesized mammogram

PD:

Percentage density

SSIM:

Structural similarity index metric

CW-SSIM:

Complex wavelet-structural similarity index metric

CB-SSIM:

Correlation-based SSIM

CB-CW-SSIM:

Correlation-based CW-SSIM

WLD:

Weber Local Descriptor

LDA:

Linear discriminant analysis

ROC:

Receiver operating characteristic

AUC:

Area under a ROC curve

CI:

Confidence interval

FDA:

Food and Drug Administration

PSNR:

Peak signal-to-noise

SIFT:

Scale-invariant feature transform

LBP:

Local binary pattern

RLS:

Run-length statistic

GLCM:

Gray level co-occurrence matrix

LOCO:

Leave-one-case-out

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Acknowledgments

The authors acknowledge Dr. Nazimah Ab Mumin from University Teknologi Mara for contributing most of the images used in this study.

Funding

This study was funded by the Electrical and Computer Systems Engineering and Advanced Engineering Platform, School of Engineering, Monash University Malaysia, and the University of Malaya Research Grant (Grant Number: PO035-2015).

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Authors and Affiliations

  1. Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Selangor, Malaysia

    Maxine Tan & Mundher Al-Shabi

  2. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA

    Maxine Tan

  3. Department of Biomedical Imaging and University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Wai Yee Chan, Leya Thomas, Kartini Rahmat & Kwan Hoong Ng

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Correspondence to Maxine Tan.

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Tan, M., Al-Shabi, M., Chan, W.Y. et al. Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment. Med Biol Eng Comput 59, 355–367 (2021). https://doi.org/10.1007/s11517-021-02313-1

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