Classification of Computerized Tomography Images of Endemic Liver Hydatid in Xinjiang Based on Decision Tree

Abstract

Objective: to assess the classification capability dealing with CT images, by means of Decision Tree classifier. Methods: the CT images were provided by the hospital was used as the data source. In this study.normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid CT images were selected.each 200 pieces. Then the texture features were extracted by gray gradient co-occurrence matrix(GGCM) and gray scale histogram. At last. Decision tree classifier were used for classification, which aimed to verify Which feature is more suitable for decision tree classification, and Parameter estimation is used to evaluate the classifier model. Results: For normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid, the classification accuracy of gray level co-occurrence matrix was 71%,69% and 69%, respectively. the classification accuracy of gray scale histogram was 74%,63.5% and 69%.the classification accuracy of comprehensive features was 75%, 70.5% and 80.5%. Conclusion: The classification accuracy of comprehensive feature is 11.5% higher than that of single features, which are more suitable for the classification of polycystic hepatic hydatid images. This algorithm can provide reference for the classification of CT images.