Hybrid ROI-Based Visualization of Medical Models

There is an increasing interest on tele-medicine and tele-diagnostic solutions based on the remote inspection of volume data coming from multimodal imaging. Client-server architectures meet these functionalities. The use of mobile devices is sometimes required due to the portability and easy maintenance. However, transmission time for the volumetric information and low performance hardware properties, make quite complex the design of efficient visualization systems on these devices. In this paper, we present a hybrid approach which is based on regions of interest (ROIs) and on a transfer-function aware compression scheme. It has a good performance in terms of bandwidth requirements and storage needs in the client device, being flexible enough to represent several materials and volume structures in the ROI. Clients store a low-resolution version of the volume data and ROI-dependent high resolution segmented information. Data must be only sent whenever a new ROI is requested, but interaction in the client is autonomous without any data transmission while a certain ROI is inspected. A benchmark is presented to compare the the proposed scheme with three existing approaches, on two different volume data models. The results show that our hybrid approach is compact, efficient and scalable, with compression rates that decrease when the size of the volume model increases.