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A Generic Framework for Internet-Based Interactive Applications of High-Resolution 3-D Medical Image Data

机译:基于Internet的高分辨率3D医学图像数据交互式应用的通用框架

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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.
机译:随着医学成像设备的进步,已经产生了大量的高分辨率3-D医学图像数据。这些高分辨率的3D数据的大小非常大,并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Internet的3D医学图像交互应用程序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3D高分辨率图像数据并在客户端站点执行图像可视化和分析,但是当高分辨率数据非常大且同时有许多用户时,这种选择是不可行的访问服务器。在本文中,我们为高分辨率3D医学图像数据的基于Internet的交互式应用程序提出了一个新颖的框架。具体来说,我们首先将整个3D数据划分为多个存储区,删除重复的存储区,然后分别压缩每个存储区。我们还为这些存储桶提出了一种索引结构,以有效地支持诸如3-D切片器和感兴趣区域之类的典型查询,并且仅传输相关的存储桶,而不传输整个高分辨率的3D医学图像数据。此外,为了更好地支持并发访问并改善平均响应时间,我们还提出了用于高效查询处理,增量传输和客户端共享的技术。我们在模拟和现实环境中的实验研究表明,提出的框架可以大大减少存储和通信需求,并可以为许多并发用户与远程高分辨率3D医学图像数据进行实时交互。

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