Abstract
In the health care industry, DICOM (Digital Imaging and Communication in Medicine) standard has become very popular for storage and transmission of digital medical images and reports. The ever-increasing size, high velocity and variety of the DICOM data collections make them more and more inefficient to be stored and queried them using a single data storage technique, e.g., a row store or a column store. In this study, we first highlight challenges in DICOM data management. We then describe HYTORMO, a new model to store and query the DICOM data. HYTORMO uses a hybrid data storage strategy that is aimed not only to leverage the advantage of both row and column stores, but also to attempt to keep a trade-off among reducing disk I/O cost, reducing tuple construction cost and reducing storage space. In addition, Bloom filters are applied to reduce network I/O cost during query processing. We prototyped our model on the top of Spark. Our preliminary experiments validate the proposed model in real DICOM datasets and show the effectiveness of our method.
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Nguyen-Cong, D., d’Orazio, L., Tran, N., Hacid, MS. (2017). Storing and Querying DICOM Data with HYTORMO. In: Wang, F., Yao, L., Luo, G. (eds) Data Management and Analytics for Medicine and Healthcare. DMAH 2016. Lecture Notes in Computer Science(), vol 10186. Springer, Cham. https://doi.org/10.1007/978-3-319-57741-8_4
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DOI: https://doi.org/10.1007/978-3-319-57741-8_4
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