Cancer area characterization by non-parametric clustering

The application of machine learning techniques to open problems in different medical research fields appears to be stimulating and fruitful, especially in the last decade. In this paper, a new method for MRI data segmentation is proposed, which aims at improving the support of medical researchers in the context of cancer therapy. In particular, our effort is focused on the processing of raw output obtained by Dynamic ContrastEnhanced MRI (DCE-MRI) techniques. Here, morphological and functional parameters are extracted, which seem indicate the local development of cancer. Our contribute consists in organizing automatically these output, separating MRI slice areas with different meaning, in a histological sense. The technique adopted is based on the Mean-Shift paradigm, and it has recently shown to be robust and useful for different and heterogeneous segmentation tasks. Moreover, the technique appears to be predisposed to numerous extensions and medical-driven optimizations.