An improved breast tissue density classification framework using bag of features model

We present a new approach to classify breast tissue density which is widely accepted to be an important risk indicator for the development of breast cancer. The computer aided diagnosis (CAD) framework developed, first segments the breast area then represents each image using the bag-of-features (BoF) approach. To represent the images, we first extract local binary patterns (LBP) features, which are then quantized to create a codebook. Second, we encode the features using the obtained codebook and a sparse coding algorithm to obtain a final image representation. Finally, we use support vector machines (SVM) classifier to carry out the classification task. In order to evaluate the efficiency of the proposed approach, we tested the framework using the digital database of screening mammograms (DDSM). The results showed that 91.25% of the samples were correctly classified. We also investigated the codebook size and selected the one that enhanced the results. Our proposal showed better performance compared to previous methods that classified the same dataset.

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