Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks
Yi Zhuang, Nan Jiang, Yongming Xu
Abstract
Computed tomography image (CTI) sequence is essentially a time‐series data that typically consists of a large amount of nearby and similar CTIs. Due to the high communication and computational costs, it is difficult to perform a progressive distributed similarity retrieval of the large CTI sequence (CTIS)s, particularly in resource‐constraint mobile telemedicine network (MTN)s. In this paper, we present a D prs method—progressive d istributed and p arallel similarity r etrieval scheme for the CTI S s in the MTN. To the best of our knowledge, there is little research on the D prs processing, especially in the MTN. Four supporting techniques (i.e., (1) PCTI‐based similarity measurement, (2) lightweight privacy‐preserving strategy, (3) SSL‐based data distribution scheme, and (4) the UDI framework) are developed. The experimental evaluation indicates that our proposed D prs method is more progressive than the state of the art, with a significant reduction in response time.