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

机译:基于Web的高分辨率3D医学图像数据交互式应用程序的框架

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With the advances in medical imaging devices, large volumes of high-resolution 3D medical image data have been produced. These high-resolution 3D data are very large in size, and severely stress storage systems and networks. Most existing Web-based 3D medical image interactive applications therefore deal with only low- or medium-resolution image data. While it is possible to download the whole 3D highresolution 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 huge, and many users concurrently access the server. In this paper, we propose a novel framework for Web-based interactive applications of high-resolution 3D medical image data. Specifically, we first partition the whole 3D data into buckets, and then compress each bucket separately. We also propose an indexing structure for these buckets to efficiently support typical queries such as 3D slicer and region of interest (ROI), and only the relevant buckets are transmitted instead of the whole highresolution 3D medical image data. Furthermore, in order to better support concurrent accesses and to improve the average response time, we also propose some techniques for bucket group access on the server side and incremental transmission. Our experimental study based on a human brain MRI data set indicates that the proposed framework can significantly reduce storage and communication requirements, and can enable real-time interaction with remote highresolution 3D 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3D医学图像数据。这些高分辨率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Web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庞大且许多用户同时访问服务器时,这种选择是不可行的。在本文中,我们为高分辨率3D医学图像数据的基于Web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,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,以有效支持典型的查询,例如3D切片器和关注区域(ROI),并且仅传输相关的存储桶,而不传输整个高分辨率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,我们还提出了一些在服务器端进行存储桶组访问和增量传输的技术。我们基于人脑MRI数据集的实验研究表明,提出的框架可以大大减少存储和通信需求,并且可以为许多并发用户提供与远程高分辨率3D医学图像数据的实时交互。

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