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An Interactive System for Computer-Aided Diagnosis of Breast Masses

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Abstract

Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists “a visual aid” in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting “abnormalities” similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 (p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.

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Acknowledgment

This work is supported in part by grants CA77850 to the University of Pittsburgh from the National Cancer Institute, National Institutes of Health, USA, and the National Distinguished Young Research Scientist Award (60788101) from National Natural Science Foundation of China.

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Authors and Affiliations

  1. Department of Radiology, University of Pittsburgh, 3362 Fifth Avenue, Pittsburgh, PA, 15213, USA

    Xingwei Wang, Dror Lederman & Bin Zheng

  2. College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Lihua Li, Wei Liu & Weidong Xu

Authors
  1. Xingwei Wang
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  2. Lihua Li
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  3. Wei Liu
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  4. Weidong Xu
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  5. Dror Lederman
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  6. Bin Zheng
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Corresponding author

Correspondence to Bin Zheng.

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Wang, X., Li, L., Liu, W. et al. An Interactive System for Computer-Aided Diagnosis of Breast Masses. J Digit Imaging 25, 570–579 (2012). https://doi.org/10.1007/s10278-012-9451-0

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