Research On Large Medical Image Transmission And Storage In Mobile Cloud Computing Environment

With the development of information technology, all walks of life are experiencing information revolution. The extensive applications of information technology in the medical field have brought with them opportunities and favorable conditions for development. During the diagnosis and treatment of diseases, doctors and nurses access enormous medical images through a terminal device to assist the diagnosis and treatment. Moreover, as the mobile device (i.e., tablet PCs, smartphones) popularized, it makes medical staff can obtain medical images at all times and in all places. To help doctors and nurses to efficiently obtain medical images, the research of efficient transmission of large medical images in the mobile network environment has been conducted in this paper.Efficient transmission of large medical images in the mobile network environment has "three highs" properties (i.e., high-dimensional, high-resolution, and high-volume), which has been a hot research topic recently. In this thesis, based on the study of the state-the art image transmission and storage mechanisms, we propose a new intelligent large medical image transmission scheme. Specifically, each medical image is first partitioned into blocks based on a coarse granularity in which each block is represented and stored with multi-resolutions. In this way, we will get many replicas for all image blocks. Furthermore, a machine learning method is adopted to optimize the storage of image replicas which is to improve storage efficiency and reduce storage costs. Based on this storage mechanism, to optimize the transmission order, an advanced transmission scheme is proposed in which each image block is set with different transmission priorities. Different transmission image replicas can be automatically selected based on the current status of the network, the information of mobile device, etc.In this thesis, we have built an efficient transmission prototype system of large medical image in a mobile environment, aiming at solving two problems:1) the intelligent medical image transmission in the mobile environment;2) adaptive medical image storage. We conducted simulation experiments and the results demonstrate that our approach is both efficient and effective.