Automatic Internal Medicine Diagnostics Using Statistical Imaging Methods

To develop a computer-aided diagnostic system for diagnosing different internal medicine diseases based on imaging methods. We focus on focal liver lesions in CT images. The diagnosing process follows the learning phase from known images. For image description, 22 first-order and 108 second-order texture features are used. They are used as input for network of Bayes classifiers. The best value of 100% success of classification between hepatocellular carcinoma and non-parasitic solitary liver cysts was achieved. The method allows discriminating between different liver diseases based on computer imaging. The method may be very useful in cases where any internal images of patients already diagnosed are available