Model generation and performance analysis in a K-B matching process of medical images

The application of a system of the blackboard type, IBIS, devoted to the processing and interpretation of real digital images, to magnetic resonance (MR) tomographic images is discussed, and the generation of a symbolic model, together with the performance and robustness of the approach, is examined. Peculiarities are represented by the mechanism of the model generation, which starts from real cases as a training set, and by the satisfactory performance reached by the matching process (based on the fuzzy sets for uncertainty management) on test cases referring to a different set of patients. The specific application considered deals with transaxial slices that are acquired from the first and second echo of a multiecho sequence, at the superorbital level, and that show signs of a lesion