The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

This paper describes a new intelligent storehouse management vehicle system based on image processing and Radio Frequency Identification (RFID) technologies to ensure the security of storehouses of logistics enterprises and improve the efficiency of storage. The vehicle is able to go ahead automatically by visual navigation in storehouses with the function of face recognition and alarm, as well as automatic statistics of commodities. Hough transformation is used to detect straight lines for angle control, and RFID based component is applied to the commodity management. The results show that our vehicle and storehouse management system can successfully fulfill various tasks with high accuracy of performance, which will lead to a practical merchandise application after further improvements.

Chronic viral hepatitis, especially viral hepatitis B (HBV), has become a widespread infectious disease in the world. China is a big power of country in HBV, and people infected in China are the largest repository of HBV, which provides extensive research resources. The Data Mining and Aided Diagnosis System of Hepatopathy (DMADSH) embarks from the clinical situations and actual needs, combines the medical knowledge with computer data comprehensive analysis and mining technology, and through the knowledge extraction of the vast amounts of patient clinical data, image characteristics, location and shape of lesion, it gets the classification of hepatitis lesions and diagnosis automatically, so as to diagnose early to liver cancer and liver cirrhosis, and support for long-term dynamic monitoring in patients of hepatopathy and other health purposes.