In colloid liver scintigraphy, pattern of diffuse parenchymal liver disease such as chronic hepatitis or cirrhosis is evaluated by size and distortion of the liver, distribution of tracer in the liver, size and activities of tracer in the spleen, visualization of the bone marrow and so on. It is not difficult to read a scintigram which shows typical pattern of normal, chronic hepatitis and cirrhosis. But in some cases it is difficult to read normal or chronic hepatitis and chronic hepatitis or cirrhosis in visual diagnosis, so we tried to use fuzzy reasoning to make differential diagnosis in chronic hepatitis (CH), bridging fibrosis (BF) and cirrhosis (LC). First, five features in colloid liver scintigrams were evaluated visually. These features were left lobe/right lobe, splenomegaly, visualization of the bone marrow, liver deformity, and distribution of tracer in the liver. Having fuzziness in these data, tendency of these features, 'small', 'medium', 'large' for instance were thought fuzzy sets and displayed in membership functions. Fuzzy reasoning was carried out using these data and fuzzy rules. Using fuzzy reasoning differential diagnosis in LC could be done in 100% ,but CH and BF could not be differentiated. So using neural network CH,BF and LC could be differentiated, therefore combining fuzzy reasoning and neural network better differential diagnosis will be expected.
