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Progressive Batch Medical Image Retrieval Processing in Mobile Wireless Networks

机译：移动无线网络中的渐进批医学图像检索处理

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摘要

This article addresses a multi-query optimization problem for distributed medical image retrieval in mobile wireless networks by exploiting the dependencies in the derivation of a retrieval evaluation plan. To the best of our knowledge, this is the first work investigating batch medical image retrieval (BMIR) processing in a mobile wireless network environment. Four steps are incorporated in our BMIR algorithm. First, when a number of retrieval requests (i.e., m retrieval images and m radii) are simultaneously submitted by users, then a cost-based dynamic retrieval (CDRS) scheduling procedure is invoked to efficiently and effectively identify the correlation among the retrieval spheres (requests) based on a cost model. Next, an index-based image set reduction (ISR) is performed at the execution-node level in parallel. Then, a refinement processing of the candidate images is conducted to get the answers. Finally, at the transmission-node level, the corresponding image fragment (IF) replicas are chosen based on an adaptive multi-resolution (AMR)-based IF replicas selection scheme, and transmitted to the user-node level by a priority-based IF replicas transmission (PIFT) scheme. The experimental results validate the efficiency and effectiveness of the algorithm in minimizing the response time and increasing the parallelism of I/O and CPU.
机译：本文通过利用检索评估计划推导中的相关性，解决了移动无线网络中分布式医学图像检索的多查询优化问题。据我们所知，这是研究移动无线网络环境中的批量医学图像检索（BMIR）处理的第一项工作。我们的BMIR算法包含四个步骤。首先，当用户同时提交多个检索请求（即m个检索图像和m半径）时，则将调用基于成本的动态检索（CDRS）调度过程，以有效地识别检索领域之间的相关性（请求）基于费用模型。接下来，在执行节点级别并行执行基于索引的图像集缩小（ISR）。然后，进行候选图像的细化处理以获得答案。最后，在传输节点级别，基于基于自适应多分辨率（AMR）的IF副本选择方案选择相应的图像片段（IF）副本，并通过基于优先级的IF将其传输到用户节点级别副本传输（PIFT）方案。实验结果验证了该算法在最小化响应时间和增加I / O和CPU并行性方面的效率和有效性。

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