In this paper, we present a blind forensic approach for the detection of global image modifications like filtering, lossy compression, scaling and so on. It is based on a new set of image features we proposed, called Histogram statistics of Reorganized Block-based Tchebichef moments (HRBT) features, and which are used as input of a set of classifiers we learned to discriminate tampered images from original ones. In this article, we compare the performances of our features with others proposed schemes from the literature in application to different medical image modalities (MRI, X-Ray ...). Experimental results show that our HRBT features perform well and in some cases better than other features.