Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks

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...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-07, Vol.2022, p.1-13
Main Authors: Zhuang, Yi, Jiang, Nan, Xu, Yongming
Format: Article
Language:English
Subjects:
Communication
Computed tomography
Efficiency
Image retrieval
Medical imaging
Mobile computing
Performance evaluation
Privacy
Response time
Semantics
Sequences
Similarity
Telemedicine
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Summary: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 Dprs method—progressive distributed and parallel similarity retrieval scheme for the CTISs in the MTN. To the best of our knowledge, there is little research on the Dprs 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 Dprs method is more progressive than the state of the art, with a significant reduction in response time.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/6458350