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The chosen platform used to build our proposal was BOINC. With mobile devices, it is also possible to reduce costs and space in computing resources for medical institutions as well as to reach places and communities with low penetration by other types of computer systems. We are currently testing the feasibility of this technology, which would allow, in the long term, health professionals to send diagnostic images and receive results making few clicks on his/her mobile devices, while examining his/her patients. The idea is to take advantage of idle computing cycles of mobile and fixed devices to process in parallel any kind of images, in particular medical images. Our research focuses on the use of mobile devices as providers of computing resources.

Users can connect their mobile devices to the grid ( smartphones, tablets, etc.) with basically two purposes: 1) to obtain access to grid resources and/or 2) to place their mobile devices at the disposal of grid users (i.e. One of these new categories is the Mobile Grid, which was defined by Furthmuller and Waldhorst as a grid that includes at least one mobile device. The inclusion of mobile devices to the grid infrastructure brought new categories of grids. Grid Computing involves the aggregation of geographically-disperse and heterogeneous resources from different organizations to solve computationally complex problems. Taking advantage of the previously mentioned smartphones capabilities, since the last decade, many research projects have addressed the problem of incorporating mobile devices to the grid. With the recent advances in low powered processors, mobile devices can perform computationally intensive operations enabling these devices to be considered as computing platforms. Also, the current capabilities of these devices have increased considerably. According to Digital Trends ( ), the number of smartphone users in the world is expected to reach 6.1 billion by 2020. The use of mobile devices has increased significantly around the world in the recent years. This article presents answers to these four challenges. By the time we started our research, the use of BOINC in mobile devices also involved two issues: a) the execution of programs in mobile devices required to modify the code to insert calls to the BOINC API, and b) the division of the image among the mobile devices as well as its merging required additional code in some BOINC components. However, parallel processing of images in mobile devices poses at least two important challenges: the execution of standard libraries for processing images and obtaining adequate performance when compared to desktop computers grids. In a previous step of this research, we selected BOINC as the infrastructure to build our mobile grid. A mobile grid is a grid that includes mobile devices as resource providers. A mobile grid can be an adequate computing infrastructure for this problem. Since some algorithms for processing images require substantial amounts of resources, one could take advantage of distributed or parallel computing.

Medical image processing helps health professionals make decisions for the diagnosis and treatment of patients.

Download previous boinc source code version