This work presents the first method of digital blind forensics within the medical imaging field with the objective to detect whether an image has been modified by some processing (e.g. filtering, lossy compression and so on). It compares two image features: the Histogram statistics of Reorganized Block-based Discrete cosine transform coefficients (HRBD), originally proposed for steganalysis purposes, and the Histogram statistics of Reorganized Block-based Tchebichef moments (HRBT). Both features serve as input of a set of SVM classifiers built in order to discriminate tampered images from original ones as well as to identify the nature of the global modification one image may have undergone. Performance evaluation, conducted in application to different medical image modalities, shows that these image features can help, independently or jointly, to blindly distinguish image processing or modifications with a detection rate greater than 70%. They also underline the complementarity of these features.