On using fuzzy c-means clustering in the fuzzy signature concept classification of liver lesions

Abstract

Liver is a very unique organ, it has double blood supply, not only through the arteries, but also through the veins. This property makes the contrast material enhanced computer tomography images show very characteristic behavior, depending on the time passed from the adjustment of the contrast material. When diagnosing a nodule in the liver by computer tomography, radiologist experts use multiple images with different delay factors, and generally, five basic characteristic properties of the nodule compared to the normal liver tissues. In the following considerations, we give a simplified model that reproduces the way medical experts take decisions, and offer a possibility to develop a computer aided diagnosis method. The classification of the nodules applies a model with fuzzy signatures, where the aggregation functions in the intermediate nodes are representing the radiologist point of view, while the membership degrees/functions at the leaves of the fuzzy signature's rooted tree are obtained from calculations applying the fuzzy c-means clustering algorithm.