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Scalable and flexible management of medical image big data

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Abstract

Digital imaging plays a critical role for image guided diagnosis and clinical trials, and the amount of image data is fast growing. There are two major requirements for image data management: scalability for massive scales and support of comprehensive queries. Traditional Picture Archiving and Communication Systems (PACS for short) are based on relational data management systems and suffer from limited scalability and query support. Therefore, new systems that support fast, scalable and comprehensive queries on image data are highly demanded. In this paper, we introduce two alternative approaches: DCMRL/XMLStore (RL/XML for short)—a parallel, hybrid relational and XML data management approach, and DCMDocStore (DOC for short)—a NoSQL document store approach. DCMRL/XMLStore manages DICOM images as binary large objects and metadata as relational tables and XML documents based on IBM DB2, which is parallelized through data partitioning. DCMDocStore manages DICOM metadata as JSON objects, and DICOM images as encoded attachments in MongoDB running on multiple nodes. We have delivered two open source systems DCMRL/XMLStore and DCMDocStore. Both systems support scalable data management and comprehensive queries. We also evaluated them with nearly one million DICOM images from National Biomedical Imaging Archive. The results show that, DCMDocStore demonstrates high data loading speed, high scalability and fault tolerance. DCMRL/XMLStore provides efficient queries, but comes with slower data loading. Traditional PACS systems have inherent limitations on flexible queries and scalability for massive amount of images.

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Acknowledgements

Funding was provided by National Science Foundation (Grant nos. ACI 1443054 and IIS 1350885) and National Institutes of Health (Grant no. K25CA181503).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Dejun Teng

  2. Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA

    Jun Kong

  3. Department of Biomedical Informatics and Department of Computer Science, Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

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  1. Dejun Teng
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Correspondence to Dejun Teng.

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Teng, D., Kong, J. & Wang, F. Scalable and flexible management of medical image big data. Distrib Parallel Databases 37, 235–250 (2019). https://doi.org/10.1007/s10619-018-7230-8

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