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

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 inclu...

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Bibliographic Details
Published in:Annals of biomedical engineering 2018-09, Vol.46 (9), p.1419-1431
Main Authors: Danala, Gopichandh, Patel, Bhavika, Aghaei, Faranak, Heidari, Morteza, Li, Jing, Wu, Teresa, Zheng, Bin
Format: Article
Language:English
Subjects:
Biochemistry
Biological and Medical Physics
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Biophysics
Breast - diagnostic imaging
Breast cancer
Breast Neoplasms - diagnostic imaging
Classical Mechanics
Contrast Media
Diagnosis
Digital computers
Digital imaging
Female
Humans
Image classification
Image processing
Image segmentation
Learning algorithms
Machine Learning
Mammography
Mammography - methods
Medical imaging
Multilayer perceptrons
Optimization
Radiographic Image Interpretation, Computer-Assisted
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Summary: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 
ISSN:0090-6964
1573-9686
DOI:10.1007/s10439-018-2044-4