Yong Jin* Pages 1 - 8 ( 8 )
Background: At present, due to the limitation of hardware, software and network transmission performance, the medical diagnosis of medical CT image equipment is easy to be carried out based on the wrong image. In addition, due to the complex structure of human organs and unpredictable lesion location, it is difficult to judge the reliability of medical CT images, spatial localization of the lesion, two-dimensional slice images and shape based on stereotypes. Therefore, how to improve the efficiency of medical CT terminal and the image quality has become the key technology to improve the satisfaction of medical diagnosis and treatment. Objective: To improve the work efficiency of medical CT terminal and medical image transmission quality, with the medical CT terminal state and service quality. Method: Firstly, from the view of throughput, packet loss rate, delay and so on, a QoS aware model for medical CT image transmission has been established. Then, with throughput, packet length, path loss, service area size, access point location, and the number of medical CT terminals, the performance change regulation of the medical CT image transmission is completed and the optimal quality of service guarantee parameters sequence is obtained. Next, the medical CT image big data autonomous collision control scheme is proposed. Results: The experimental and mathematical results verify the real-time performance, reliability, effectiveness and feasibility of the proposed medical CT image transmission anti-collision mechanism. Conclusion: The proposed scheme can satisfy the high-quality high demand for data transmission at the same time, according to a variety of user experience demand and real-time adjustment of medical CT terminal working state, which provides effective data quality assurance and optimization of the network source distribution, and also enhances the quality of medical image data transmission service.
Medical Computed Tomography, medical image transmission, Quality of Service aware, big data network, autonomous anti-collision control
School of Computer Science & Engineering, Changshu Institute of Technology, Changshu 215500