A multiresolution analysis framework for breast tumor classification based on DCE-MRI

In this paper, a multiresolution approach is proposed for texture characterization of breast tumors in dynamic contrast-enhanced magnetic resonance images. The decomposition scheme represented by the stationary wavelet transform (SWT) is investigated in terms of its´ ability to discriminate between malignant and benign tumors. The mean and entropy of the detail subimages produced for the specific decomposition scheme are used as texture features. The extracted features are subsequently provided into a linear classifier in a leave-one-out cross-validation setting. The experimental results for the proposed features exhibit high performance, when compared to the existing approaches, with the classification accuracy approaching 0.91.