Differentiating benign and malignant mass and non-mass lesions in breast DCE-MRI using normalized frequency-based features

Int J Comput Assist Radiol Surg. 2020 Feb;15(2):297-307. doi: 10.1007/s11548-019-02103-z. Epub 2019 Dec 14.


Purpose: In this study, we propose a new computer-aided diagnosis (CADx) to distinguish between malign and benign mass and non-mass lesions in breast DCE-MRI. For this purpose, we introduce new frequency textural features.

Methods: In this paper, we propose novel normalized frequency-based features. These are obtained by applying the dual-tree complex wavelet transform to MRI slices containing a lesion for specific decomposition levels. The low-pass and band-pass frequency coefficients of the dual-tree complex wavelet transform represent the general shape and texture features, respectively, of the lesion. The extraction of these features is computationally efficient. We employ a support vector machine to classify the lesions, and investigate modified cost functions and under- and oversampling strategies to handle the class imbalance.

Results: The proposed method has been tested on a dataset of 80 patients containing 103 lesions. An area under the curve of 0.98 for the mass and 0.94 for the non-mass lesions is obtained. Similarly, accuracies of 96.9% and 89.8%, sensitivities of 93.8% and 84.6% and specificities of 98% and 92.3% are obtained for the mass and non-mass lesions, respectively.

Conclusion: Normalized frequency-based features can characterize benign and malignant lesions efficiently in both mass- and non-mass-like lesions. Additionally, the combination of normalized frequency-based features and three-dimensional shape descriptors improves the CADx performance.

Keywords: Complex wavelet; Computer-aided diagnosis; Imbalanced data; Magnetic resonance imaging (MRI); Mass and non-mass breast lesions.

MeSH terms

  • Algorithms
  • Breast / diagnostic imaging*
  • Breast Neoplasms / diagnostic imaging*
  • Diagnosis, Computer-Assisted / methods*
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods
  • Sensitivity and Specificity
  • Support Vector Machine
  • Wavelet Analysis