A Study on the Classification of Ultrasonic Liver Image Feature Vectors and the Design of Diagnosis System

초음파 간영상의 특징벡터 분류 및 진단시스템 구현에 관한 연구

Since one property(i.e. coarseness, orientation, regularity, granularity etc.) of ultrasound liver images was not sufficiently enough to classify the characteristics of livers, we used the multi-feature vectors from ultrasound images to diagnose the liver disease. The proposed classifier, which uses the multi-feature vectors and Bayes decision rule, performed well for the classification of normal, fat and cirrhosis liver. In our simulation, we used the Battacharyya distance and Hotelling Trace Criterion to select the best multi-feature vectors for the classifier and obtained less classification errors than other methods using single feature vector.