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Annals of Biomedical Engineering

, Volume 46, Issue 9, pp 1419–1431 | Cite as

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

  • Gopichandh Danala
  • Bhavika Patel
  • Faranak Aghaei
  • Morteza Heidari
  • Jing Li
  • Teresa Wu
  • Bin Zheng
Article

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.

Keywords

Breast cancer diagnosis Computer-aided diagnosis (CAD) Contrast-enhanced digital mammography (CEDM) Classification of breast masses Segmentation of breast mass regions Performance comparison 

Notes

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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Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringUniversity of OklahomaNormanUSA
  2. 2.Department of RadiologyMayo ClinicPhoenixUSA
  3. 3.School of Computing, Informatics, Decision Systems EngineeringArizona State UniversityTempeUSA

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