Effective and efficient crowd-assisted similarity retrieval of medical images in resource-constraint Mobile telemedicine systems

Wireless Communications and Mobile Computing

2022

Self Cite

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.

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Section: Pr Jmentioning

confidence: 99%

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

Wireless Communications and Mobile Computing

2022

Self Cite

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“…For telemedicine applications, Chitra et al [ 28 ] suggested an enhanced retrieval approach for brain images utilizing carrier frequency offset adjusted OFDM technique. To solve the ‘ semantic gap ’, Jiang et al [ 29 ] introduced a novel framework of mobile similarity retrieval of medical images based on a crowdsourcing model.…”

Section: Introductionmentioning

confidence: 99%

Progressive privacy-preserving batch retrieval of lung CT image sequences based on edge-cloud collaborative computation

2022

PLoS ONE

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Background A computer tomography image (CI) sequence can be regarded as a time-series data that is composed of a great deal of nearby and similar CIs. Since the computational and I/O costs of similarity measure, encryption, and decryption calculation during a similarity retrieval of the large CI sequences (CIS) are extremely high, deploying all retrieval tasks in the cloud, however, will lead to excessive computing load on the cloud, which will greatly and negatively affect the retrieval performance. Methodologies To tackle the above challenges, the paper proposes a progressive privacy-preserving Batch Retrieval scheme for the lung CISs based on edge-cloud collaborative computation called the BRS method. There are four supporting techniques to enable the BRS method, such as: 1) batch similarity measure for CISs, 2) CIB-based privacy preserving scheme, 3) uniform edge-cloud index framework, and 4) edge buffering. Results The experimental results reveal that our method outperforms the state-of-the-art approaches in terms of efficiency and scalability, drastically reducing response time by lowering network communication costs while enhancing retrieval safety and accuracy.

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Privacy-Preserving Distributed Similarity Retrieval of Large CT Image Sequence Database in Mobile Telemedicine Networks

2022

IEEE Access