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SVM-based CBIR of breast masses on mammograms

Mammography is currently the dominant imaging modality for the early detection of breast cancer. However, its robustness in distinguishing malignancy is relatively low, resulting in a large number of unnecessary biopsies. A computer-aided diagnosis (CAD) scheme, capable of visually justifying its results, is expected to aid the decision made by radiologists. Content-based image retrieval (CBIR) accounts for a promising paradigm in this direction. Facing this challenge, we introduce a CBIR scheme that utilizes the extracted features as input to a support vector machine (SVM) ensemble. The final features used for CBIR comprise the participation value of each SVM. The retrieval performance of the proposed scheme has been evaluated quantitatively on the basis of the standard measures. In the experiments, a set of 90 mammograms is used, derived from a widely adopted digital database for screening mammography. The experimental results show the improved performance of the proposed scheme.