Exploring publish/subscribe, multilevel cloud elasticity, and data compression in telemedicine.

Background And Objective: Multiple medical specialties rely on image data, typically following the Digital Imaging and Communications in Medicine (DICOM) ISO 12052 standard, to support diagnosis through telemedicine. Remote analysis by different physicians requires the same image to be transmitted simultaneously to different destinations in real-time. This scenario poses a need for a large number of resources to store and transmit DICOM images in real-time, which has been explored using some cloud-based solutions. However, these solutions lack strategies to improve the performance through the cloud elasticity feature. In this context, this article proposes a cloud-based publish/subscribe (PubSub) model, called PS2DICOM, which employs multilevel resource elasticity to improve the performance of DICOM data transmissions.

Methods: A prototype is implemented to evaluate PS2DICOM. A PubSub communication model is adopted, considering the coexistence of two classes of users: (i) image data producers (publishers); and (ii) image data consumers (subscribers). PS2DICOM employs a cloud infrastructure to guarantee service availability and performance through resource elasticity in two levels of the cloud: (i) brokers and (ii) data storage. In addition, images are compressed prior to the transmission to reduce the demand for network resources using one of three different algorithms: (i) DEFLATE, (ii) LZMA, and (iii) BZIP2. PS2DICOM employs dynamic data compression levels at the client side to improve network performance according to the current available network throughput.

Results: Results indicate that PS2DICOM can improve transmission quality, storage capabilities, querying, and retrieving of DICOM images. The general efficiency gain is approximately 35% in data sending and receiving operations. This gain is resultant from the two levels of elasticity, allowing resources to be scaled up or down automatically in a transparent manner.

Conclusions: The contributions of PS2DICOM are twofold: (i) multilevel cloud elasticity to adapt the computing resources on demand; (ii) adaptive data compression to meet the network quality and optimize data transmission. Results suggest that the use of compression in medical image data using PS2DICOM can improve the transmission efficiency, allowing the team of specialists to communicate in real-time, even when they are geographically distant.