A Bag of Features Approach for CEUS Liver Lesions Investigation

In this work a novel approach for CEUS based diagnosis is presented. We propose a spatial/image-based method using a parallel and hierarchical system architecture. As a feature extraction stage, we propose the Bag of Features (BoF) algorithm which treats image features as a bag of visual words. It is followed by a multiclass SVM classifier trained separately for each phase of the ultrasound investigation. A soft voting scheme has been proposed for the information fusion of the individual phase classifiers. The preliminary evaluation shows promising qualitative results of our approach on samples of a newly introduced CEUS dataset. Using only 550 images, (5 liver lesions x 10 pictures/lesion x 11 patients) an average accuracy of 64% has been obtained for a leave-one patient-out procedure.