Computer image analysis of ultrasound images for discriminating and grading liver parenchyma disease employing a hierarchical decision tree scheme and the multilayer perceptron neural network classifier.

Differential diagnosis of liver parenchyma disease and grading of the hepatic disease on ultrasound is a common radiological problem that influences patient management. The aim of this study was to apply image analysis methods on ultrasound images for discriminating liver cirrhosis from fatty liver infiltration and for grading hepatic disease, which is important in the management of the patients. Ultrasound images of histologically confirmed 18 livers with cirrhosis, 37 livers with fatty infiltration, and 24 normal livers of healthy volunteers were selected and were digitized for further computer processing. Twenty two textural features were calculated from small matrix samples selected from the ultrasound image matrix of the liver parenchyma. These features were used in the design a three level hierarchical decision tree classification scheme, employing the multilayer perceptron neural network classifier at each hierarchical tree level. At the first tree level, classification accuracy for distinguishing normal from abnormal livers was 93.7%, at the second level the accuracy for discriminating cirrhosis from fatty infiltration was 90.9%, and at the third level the accuracy in distinguishing between low and high grading liver cirrhosis or fatty infiltration was 94.1% and 84.9% respectively. The proposed computer software system may be of value to the radiologists in assessing liver parenchyma disease.