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An application of artificial intelligence in the field of automatization in medicine is described. A computer-aided diagnostic (CAD) system for focal liver lesions automatic classification in CT images is being developed. The texture analysis methods are used for the classification of hepatocellular cancer and liver cysts. CT contrast enhanced images of 20 adult subjects with hepatocellular carcinoma or with non-parasitic solitary liver cyst were used as entry data. A total number of 130 spatial and second-order probabilistic texture features were computed from the images. Ensemble of Bayes classifiers was used for the tissue classification. Classification success rate was as high as 100% when estimated by leave-one-out method. This high success rate was achieved with as few as one optimal descriptive feature representing the average deviation of horizontal curvature computed from original pixel gray levels. This promising result allows next amplification of this approach in distinguishing more types of liver diseases from CT images and its further integration to PACS and hospital information systems.

Artificial Intelligence Methods Application in Liver Diseases Classification from CT Images