Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

Abstract

BackgroundDigital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading.MethodsFirstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system.ResultsThe results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85.ConclusionsCompared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

Investigating the performance of a CAD<inf>x</inf> scheme for mammography in specific BIRADS categories

“…Specifically, Papadopoulos et al [10] reported a value for A z equal to 0.81, while Chen et al [11] reported A z = 0.92. Jian et al [12] did not include the A z values in their study, that is the most powerful metric for comparison in two class classification problems. Instead, they reported values for the accuracy that was found to be equal to 95.8% without using wavelets and 100% using a dual-tree complex wavelet transform.…”

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