Liu Danzhou, Hua Kien A, Sugaya Kiminobu
School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA.
IEEE Trans Inf Technol Biomed. 2008 Sep;12(5):618-26. doi: 10.1109/TITB.2008.923770.
With the advances in medical imaging devices, large volumes of high-resolution 3-D medical image data have been produced. These high-resolution 3-D data are very large in size, and severely stress storage systems and networks. Most existing Internet-based 3-D medical image interactive applications therefore deal with only low- or medium-resolution image data. While it is possible to download the whole 3-D high-resolution image data from the server and perform the image visualization and analysis at the client site, such an alternative is infeasible when the high-resolution data are very large, and many users concurrently access the server. In this paper, we propose a novel framework for Internet-based interactive applications of high-resolution 3-D medical image data. Specifically, we first partition the whole 3-D data into buckets, remove the duplicate buckets, and then, compress each bucket separately. We also propose an index structure for these buckets to efficiently support typical queries such as 3-D slicer and region of interest, and only the relevant buckets are transmitted instead of the whole high-resolution 3-D medical image data. Furthermore, in order to better support concurrent accesses and to improve the average response time, we also propose techniques for efficient query processing, incremental transmission, and client sharing. Our experimental study in simulated and realistic environments indicates that the proposed framework can significantly reduce storage and communication requirements, and can enable real-time interaction with remote high-resolution 3-D medical image data for many concurrent users.
随着医学成像设备的进步,已产生了大量高分辨率的三维医学图像数据。这些高分辨率的三维数据规模非常大,给存储系统和网络带来了巨大压力。因此,大多数现有的基于互联网的三维医学图像交互应用仅处理低分辨率或中等分辨率的图像数据。虽然可以从服务器下载整个高分辨率三维图像数据并在客户端进行图像可视化和分析,但当高分辨率数据非常大且许多用户同时访问服务器时,这种方式并不可行。在本文中,我们提出了一种用于基于互联网的高分辨率三维医学图像数据交互应用的新颖框架。具体而言,我们首先将整个三维数据划分为多个块,去除重复的块,然后分别对每个块进行压缩。我们还为这些块提出了一种索引结构,以有效地支持诸如三维切片器和感兴趣区域等典型查询,并且只传输相关的块而不是整个高分辨率三维医学图像数据。此外,为了更好地支持并发访问并提高平均响应时间,我们还提出了高效查询处理、增量传输和客户端共享等技术。我们在模拟和现实环境中的实验研究表明,所提出的框架可以显著降低存储和通信需求,并能够使许多并发用户与远程高分辨率三维医学图像数据进行实时交互。