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 (SVM) 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 (AUC) 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. Conclusions: 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 improve the CADx performance.
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