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Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms

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Abstract

Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron based machine learning classifiers that integrate with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. When applying CAD to DES and LE images with original segmentation, areas under ROC curves (AUC) were 0.759 ± 0.053 and 0.753 ± 0.047, respectively. After mapping the mass regions optimally segmented on DES images to LE images, AUC significantly increased to 0.848 ± 0.038 (p < 0.01). Study demonstrated that DES images eliminated overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. The study demonstrated that applying a novel approach to optimally map mass region segmented from DES images to LE images enabled CAD to yield significantly improved performance.

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Acknowledgment

This work is supported in part by Grant R01 CA197150 from the National Cancer Institute, National Institutes of Health, USA.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, University of Oklahoma, 101 David L Boren Blvd, Norman, OK, 73019, USA

    Gopichandh Danala, Faranak Aghaei, Morteza Heidari & Bin Zheng

  2. Department of Radiology, Mayo Clinic, Phoenix, AZ, 85054, USA

    Bhavika Patel

  3. School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA

    Jing Li & Teresa Wu

Authors
  1. Gopichandh Danala
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  2. Bhavika Patel
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  3. Faranak Aghaei
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  4. Morteza Heidari
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  5. Jing Li
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  6. Teresa Wu
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  7. Bin Zheng
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Correspondence to Bin Zheng.

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Associate Editor Ender A Finol oversaw the review of this article.

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Danala, G., Patel, B., Aghaei, F. et al. Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms. Ann Biomed Eng 46, 1419–1431 (2018). https://doi.org/10.1007/s10439-018-2044-4

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  • DOI: https://doi.org/10.1007/s10439-018-2044-4

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