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A data colocation grid framework for big data medical image processing: backend design
Author(s): Shunxing Bao; Yuankai Huo; Prasanna Parvathaneni; Andrew J. Plassard; Camilo Bermudez; Yuang Yao; Ilwoo Lyu; Aniruddha Gokhale; Bennett A. Landman
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Paper Abstract

When processing large medical imaging studies, adopting high performance grid computing resources rapidly becomes important. We recently presented a "medical image processing-as-a-service" grid framework that offers promise in utilizing the Apache Hadoop ecosystem and HBase for data colocation by moving computation close to medical image storage. However, the framework has not yet proven to be easy to use in a heterogeneous hardware environment. Furthermore, the system has not yet validated when considering variety of multi-level analysis in medical imaging. Our target design criteria are (1) improving the framework’s performance in a heterogeneous cluster, (2) performing population based summary statistics on large datasets, and (3) introducing a table design scheme for rapid NoSQL query. In this paper, we present a heuristic backend interface application program interface (API) design for Hadoop and HBase for Medical Image Processing (HadoopBase-MIP). The API includes: Upload, Retrieve, Remove, Load balancer (for heterogeneous cluster) and MapReduce templates. A dataset summary statistic model is discussed and implemented by MapReduce paradigm. We introduce a HBase table scheme for fast data query to better utilize the MapReduce model. Briefly, 5153 T1 images were retrieved from a university secure, shared web database and used to empirically access an in-house grid with 224 heterogeneous CPU cores. Three empirical experiments results are presented and discussed: (1) load balancer wall-time improvement of 1.5-fold compared with a framework with built-in data allocation strategy, (2) a summary statistic model is empirically verified on grid framework and is compared with the cluster when deployed with a standard Sun Grid Engine (SGE), which reduces 8-fold of wall clock time and 14-fold of resource time, and (3) the proposed HBase table scheme improves MapReduce computation with 7 fold reduction of wall time compare with a naïve scheme when datasets are relative small. The source code and interfaces have been made publicly available.

Paper Details

Date Published: 6 March 2018
PDF: 10 pages
Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790A (6 March 2018); doi: 10.1117/12.2293694
Show Author Affiliations
Shunxing Bao, Vanderbilt Univ. (United States)
Yuankai Huo, Vanderbilt Univ. (United States)
Prasanna Parvathaneni, Vanderbilt Univ. (United States)
Andrew J. Plassard, Vanderbilt Univ. (United States)
Camilo Bermudez, Vanderbilt Univ. (United States)
Yuang Yao, Vanderbilt Univ. (United States)
Ilwoo Lyu, Vanderbilt Univ. (United States)
Aniruddha Gokhale, Vanderbilt Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 10579:
Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
Jianguo Zhang; Po-Hao Chen, Editor(s)

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