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


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.


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



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


  1. 1.
    Aghaei, F., M. Tan, A. B. Hollingsworth, and B. Zheng. Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy. J. Magn. Reson. Imaging 44:1099–1106, 2016.CrossRefGoogle Scholar
  2. 2.
    Baltzer, P. A. T., M. Benndorf, M. Dietzel, M. Gajda, I. B. Runnebaum, and W. A. Kaiser. False-positive findings at contrast-enhanced breast MRI: a BI-RADS descriptor study. Am. J. Roentgenol. 194:1658–1663, 2010.CrossRefGoogle Scholar
  3. 3.
    Berg, W. A., Z. Zhang, D. Lehrer, and R. A. Jong. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. J. Am. Med. Assoc. 307:1394–1404, 2012.CrossRefGoogle Scholar
  4. 4.
    Brodersen, J., and V. Siersma. Long-term psychosocial consequences of false-positive mammography screening. Ann. Fam. Med. 11:106–115, 2013.CrossRefGoogle Scholar
  5. 5.
    Carney, P. A., D. L. Miglioretti, B. C. Yankaskas, K. Kerlikowske, R. Rosenberg, C. M. Rutter, B. M. Geller, L. A. Abraham, S. H. Taplin, M. M. Dignan, and R. Gary Cutter. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann. Intern. Med. 138:168–175, 2003.CrossRefGoogle Scholar
  6. 6.
    Chawla, N. V., K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16:321–357, 2002.CrossRefGoogle Scholar
  7. 7.
    Danala, G., T. Thai, C. C. Gunderson, K. M. Moxley, K. Moore, R. S. Mannel, H. Liu, B. Zheng, and Y. Qiu. Applying quantitative CT image feature analysis to predict response of ovarian cancer patients to chemotherapy. Acad. Radiol. 24:1233–1239, 2017.CrossRefGoogle Scholar
  8. 8.
    Dromain, C., C. Balleyguier, S. Muller, M.-C. Mathieu, F. Rochard, P. Opolon, and R. Sigal. Evaluation of tumor angiogenesis of breast carcinoma using contrast-enhanced digital mammography. Am. J. Roentgenol. 187:W528–W537, 2006.CrossRefGoogle Scholar
  9. 9.
    Eltonsy, N. H., G. D. Tourassi, A. S. Elmaghraby, and S. Member. A concentric morphology model for the detection of masses in mammography. IEEE Trans. Med. Imaging 26:880–889, 2007.CrossRefGoogle Scholar
  10. 10.
    Gur, D., J. Stalder, and L. A. Hardesty. CAD performance on sequentially ascertained mammographic examinations of masses: an assessment. Radiology 233:418–423, 2004.CrossRefGoogle Scholar
  11. 11.
    Kriege, M., C. T. M. Brekelmans, C. Boetes, and P. E. Besnard. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N. Engl. J. Med. 351:427–437, 2004.CrossRefGoogle Scholar
  12. 12.
    Laya, M. B., E. B. Larson, S. H. Taplin, and E. White. Effect of estrogen replacement therapy on the specificity and sensitivity of screening mammography. J. Natl. Cancer Inst. 88:643–649, 1996.CrossRefGoogle Scholar
  13. 13.
    Lee, A. Y., D. J. Wisner, S. Aminololama-Shakeri, V. A. Arasu, S. A. Feig, J. Hargreaves, H. Ojeda-Fournier, L. W. Bassett, C. J. Wells, J. De Guzman, C. I. Flowers, J. E. Campbell, S. L. Elson, H. Retallack, and B. N. Joe. Inter-reader variability in the use of BI-RADS descriptors for suspicious findings on diagnostic mammography: a multi-institution study of 10 academic radiologists. Acad. Radiol. 24:60–66, 2017.CrossRefGoogle Scholar
  14. 14.
    Li, Q., and K. Doi. Reduction of bias and variance for evaluation of computer-aided diagnostic schemes. Med. Phys. 33:868–875, 2006.CrossRefGoogle Scholar
  15. 15.
    Lu, W., Z. Li, and J. Chu. A novel computer-aided diagnosis system for breast MRI based on feature selection and ensemble learning. Comput. Biol. Med. 83:157–165, 2017.CrossRefGoogle Scholar
  16. 16.
    Mandelson, T. M. Breast density as a predictor of breast cancer risk. J. Natl. Cancer Inst. 12:1081–1087, 2000.CrossRefGoogle Scholar
  17. 17.
    Oliver, A., J. Freixenet, J. Martí, E. Pérez, J. Pont, E. R. E. Denton, and R. Zwiggelaar. A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14:87–110, 2010.CrossRefGoogle Scholar
  18. 18.
    Patel, B. K., S. Ranjbar, T. Wu, B. A. Pockaj, J. Li, N. Zhang, M. Lobbes, B. Zhang, and J. R. Mitchell. Computer-aided diagnosis of contrast-enhanced spectral mammography: a feasibility study. Eur. J. Radiol. 98:207–213, 2018.CrossRefGoogle Scholar
  19. 19.
    Peer, P. G. M., A. L. M. Verbeek, H. Straatman, J. H. C. L. Hendriks, and R. Holland. Age-specific sensitivities of mammographic screening for breast cancer. Breast Cancer Res. 38:153–160, 1996.CrossRefGoogle Scholar
  20. 20.
    Qiu, Y., S. Yan, R. R. Gundreddy, Y. Wang, S. Cheng, H. Liu, and B. Zheng. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J. Xray. Sci. Technol. 25:751–763, 2017.Google Scholar
  21. 21.
    Rebecca, A. H., K. Kerlikowske, C. I. Flowers, B. C. Yankaskas, Z. Weiwei, and D. L. Miglioretti. Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography. Ann. Intern. Med. 155:481–492, 2011.CrossRefGoogle Scholar
  22. 22.
    Saeys, Y., I. Inza, and P. Larranaga. A review of feature selection techniques in bioinformatics. Bioinformatics 23:2507–2517, 2007.CrossRefGoogle Scholar
  23. 23.
    Tan, M., F. Aghaei, Y. Wang, and B. Zheng. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions. Phys. Med. Biol. 62:358–376, 2017.CrossRefGoogle Scholar
  24. 24.
    Tan, M., B. Zheng, J. K. Leader, and D. Gur. Association between changes in mammographic image features and risk for near-term breast cancer development. IEEE Trans. Med. Imaging 35:1719–1728, 2016.CrossRefGoogle Scholar
  25. 25.
    Wang, Y., F. Aghaei, A. Zarafshani, Y. Qiu, W. Qian, and B. Zheng. Computer-aided classification of mammographic masses using visually sensitive image features. J. Xray. Sci. Technol. 25:171–186, 2017.Google Scholar
  26. 26.
    Wang, X., D. Lederman, J. Tan, X. H. Wang, and B. Zheng. Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry. Med. Eng. Phys. 27:934–942, 2011.CrossRefGoogle Scholar
  27. 27.
    Weaver, D. L., R. D. Rosenberg, W. E. Barlow, L. Ichikawa, P. A. Carney, K. Kerlikowske, D. S. Buist, B. M. Geller, C. R. Key, S. J. Maygarden, and R. Ballard-Barbash. Pathologic findings from the breast cancer surveillance consortium. Cancer 106(4):732–742, 2006.CrossRefGoogle Scholar
  28. 28.
    Witten, I. H., E. Frank, and M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). Amsterdam: Elsevier, 2011.Google Scholar
  29. 29.
    Zheng, B., J. H. Sumkin, M. L. Zuley, D. Lederman, X. Wang, and D. Gur. Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment. Br. J. Radiol. 85:e153–e161, 2012.CrossRefGoogle Scholar

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

Personalised recommendations