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

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Abstract

This paper presents an effective and efficient framework for Crowd-assisted Mobile Similarity Retrieval of the large-scale medical images in the resource-constraint mobile telemedicine systems (MTS), called the CMSR. The CMSR processing works as follows: when a user submits a retrieval medical image IR, a buffer checking processing is first invoked to check if the full (or partial) retrieval results have been cached in the buffer previously. After that, a parallel image data filtering and refinement processing is conducted at a master node level. Finally, the candidate images are concurrently validated by a mCrowd system to derive an answer set that is transmitted to the retrieval node. To better facilitate the effective and efficient CMSR processing, three enabling techniques, i.e., category-based image data interleaving placement scheme, hindex-support image filtering algorithm and a kNN-based buffering scheme are developed. To improve the retrieval throughput, finally, we propose an extension of the CMSR method called mCMSR to optimize the multiple CMSRs. The experimental results show that the performances of the CMSR and the mCMSR methods are: 1) effective in improving the retrieval accuracy; 2) efficient in minimizing the response time by decreasing the network transmission cost while increasing the parallelism of I/O and CPU.

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Notes

  1. Generally speaking, a MUA refers to the image area that contains a lesion organ.

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Acknowledgements

This work is partially supported by the Program of National Natural Science Foundation of China under Grant No. 61540064; the Program of Natural Science Foundation of Zhejiang Province under grant No. LY18F020006; the Program of Medical and Health Science and Technology Plan of Zhejiang Province under grant No. 2019RC070.

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Authors and Affiliations

  1. Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China

    Nan Jiang

  2. College of Computer and Information Engineering, Zhejiang Gongshang University, Apart.502, Unit 2, Blg.7, Qinchunfang, Hangzhou, People’s Republic of China

    Yi Zhuang

  3. Faculty of Education, The University of Hong Kong, Pok Fu Lam, Hong Kong, HKSAR, People’s Republic of China

    Dickson K. W. Chiu

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  1. Nan Jiang
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  3. Dickson K. W. Chiu
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Correspondence to Yi Zhuang.

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Jiang, N., Zhuang, Y. & Chiu, D.K.W. Effective and efficient crowd-assisted similarity retrieval of medical images in resource-constraint Mobile telemedicine systems. Multimed Tools Appl 79, 19893–19923 (2020). https://doi.org/10.1007/s11042-020-08755-3

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