of the crucial and mostly innovative phases of the method according to the present invention in fact until now other cad have used many features but have calculated only on the roi associated to a correspondent lesion while the method according to this invention exploits the u201c variability u201d of the features calculated on the roi and subrois related to the same lesion such procedure allows an analysis of the tumour u201c growth u201d thus pointing out early the presence of tumoral lesions it was in fact observed that the spatial variability of the features for several values s j of the parameter is correlated to the temporal evolution of the rois and therefore to the possible tumour in such circumstances the behaviour of the pathological rois is different from the healthy rois because of the speed and the tumour expansion characteristics themselves such as a greater ramification and penetration over a time period within this step the features calculated in the previous phase are used to classify the rois with the purpose to assign to each roi a pathology probability more specifically for the set of values the t features are computed for a level 1 of the parameter to assign a u201c current u201d pathology probability while for the set of values the t features are computed for a level 0 of the parameter to assign an u201c evolution u201d pathology probability the rois u201c separation u201d can be obtained with any of the classification algorithms.