NDLI: ISP: an optimal out-of-core image-set processing streaming architecture for parallel heterogeneous systems.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Ha, Linh Khanh Krüger, Jens Dihl Comba, João Luiz Silva, Cláudio T. Joshi, Sarang
Description	Country affiliation: United States Author Affiliation: Ha LK ( Scientific Imaging and Computing Institute, University of Utah, 72 S Central Campus Dr, WEB, Room 3692, Salt Lake City, UT 84112, USA. lha@sci.utah.edu)
Abstract	Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.
File Format	HTM / HTML
ISSN	10772626
Issue Number	6
Volume Number	18
e-ISSN	19410506
Journal	IEEE Transactions on Visualization and Computer Graphics
Language	English
Publisher	IEEE
Publisher Date	2012-06-01
Publisher Place	United States
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Biomedical Informatics Algorithms Computer Graphics Image Processing, Computer-assisted Methods Brain Anatomy & Histology Pathology Databases, Factual Diagnostic Imaging Humans Models, Theoretical Regression Analysis Journal Article Research Support, N.i.h., Extramural Research Support, Non-u.s. Gov't Research Support, U.s. Gov't, Non-p.h.s.
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Signal Processing Computer Vision and Pattern Recognition Software

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