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Medical image sharing is an important problem in modern radiology, with wide applications in Internet and mobile devices. Some important features need to be added and optimized to medical image sharing. In this paper, we present an extensible Web Access to DICOM Persistent Objects (WADO) middleware based on image cache and real-time Web monitor technology for regional medical image sharing. We first develop the extension method of WADO standard and workflow of extended WADO service. Then, we design a medical image cache method to improve the performance of medical image on-demand transmission. Using the real-time monitor can discover the performance bottlenecks and optimized critical points. The experimental results show that the middleware effectively delivers medical images and reports to Web clients over the Internet, regardless of the platform used for access. It can be deployed in one hospital to provide WADO service to medical workers and also can be applied to regional picture archiving and communication systems (PACS) to transmit medical images and reports to Internet users in a way that is transparent to end-user applications.

BackgroundSubstance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease.MethodsTo study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts.ResultsAfter using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods.ConclusionThus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.