High performance computing techniques for scaling image analysis workflows

Authors:
Patrick M. Widener;Tahsin Kurc;Wenjin Chen;Fusheng Wang;Lin Yang;Jun Hu;Vijay Kumar;Vicky Chu;Lee Cooper;Jun Kong;Ashish Sharma;Tony Pan;Joel H. Saltz;David J. Foran
Affiliations:
Center for Comprehensive Informatics, Emory University;Center for Comprehensive Informatics, Emory University;Center for Biomedical Imaging & Informatics, UMDNJ-Robert Wood Johnson Medical School;Center for Comprehensive Informatics, Emory University;Center for Biomedical Imaging & Informatics, UMDNJ-Robert Wood Johnson Medical School;Center for Biomedical Imaging & Informatics, UMDNJ-Robert Wood Johnson Medical School;Department of Computer Science and Engineering, Ohio State University;Center for Biomedical Imaging & Informatics, UMDNJ-Robert Wood Johnson Medical School;Center for Comprehensive Informatics, Emory University;Center for Comprehensive Informatics, Emory University;Center for Comprehensive Informatics, Emory University;Center for Comprehensive Informatics, Emory University;Center for Comprehensive Informatics, Emory University;Center for Biomedical Imaging & Informatics, UMDNJ-Robert Wood Johnson Medical School
Venue:
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2
Year:
2010

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Abstract

Biomedical images are intrinsically complex with each domain and modality often requiring specialized knowledge to accurately render diagnosis and plan treatment. A general software framework that provides access to high-performance resources can make possible high-throughput investigations of micro-scale features as well as algorithm design, development and evaluation. In this paper we describe the requirements and challenges of supporting microscopy analyses of large datasets of high-resolution biomedical images. We present high-performance computing approaches for storage and retrieval of image data, image processing, and management of analysis results for additional explorations. Lastly, we describe issues surrounding the use of high performance computing for scaling image analysis workflows.