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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rimm, D.L., Camp, R.L., Charette, L.A., Costa, J., Olsen, D.A., Reiss, M.: Tissue microarray: A new technology for amplification of tissue resources. Cancer Journal 7(1), 24–31 (2001)
Zhang, X., Pan, T., Catalyurek, U., Kurc, T., Saltz, J.: Serving Queries to Multi-Resolution Datasets on Disk-based Storage Clusters. In: The Proceedings of 4th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2004), Chicago, IL (April 2004)
Kumar, V.S., Rutt, B., Kurc, T.M., Catalyurek, U.V., Pan, T.C., Chow, S., Lamont, S., Martone, M., Saltz, J.H.: Large-scale biomedical image analysis in grid environments. IEEE Transactions on Information Technology in Biomedicine 12(2), 154–161 (2008)
Kumar, V., Kurc, T., Ratnakar, V., Kim, J., Mehta, G., Vahi, K., Nelson, Y., Sadayappan, P., Deelman, E., Gil, Y., Hall, M., Saltz, J.: Parameterized specification, configuration and execution of data-intensive scientific workflows. Cluster Computing (April 2010)
Beynon, M., Chang, C., Catalyurek, U., Kurc, T., Sussman, A., Andrade, H., Ferreira, R., Saltz, J.: Processing large-scale multi-dimensional data in parallel and distributed environments. Parallel Comput. 28(5), 827–859 (2002)
Kumar, V., Kurc, T., Saltz, J., Abdulla, G., Kohn, S., Matarazzo, C.: Architectural Implications for Spatial Object Association Algorithms. In: The 23rd IEEE International Parallel and Distributed Processing Symposium (IPDPS 2009), Rome, Italy (May 2009)
Gray, J., Nieto-Santisteban, M.A., Szalay, A.S.: The zones algorithm for finding points-near-a point or cross-matching spatial datasets. CoRR, abs/cs/0701171 (2007)
Kurc, T., Hastings, S., Kumar, S., et al.: HPC and Grid Computing for Integrative Biomedical Research. International Journal of High Performance Computing Applications, Special Issue of the Workshop on Clusters and Computational Grids for Scientific Computing (August 2009)
Oster, S., Langella, S., Hastings, S., Ervin, D., Madduri, R., Phillips, J., Kurc, T., Siebenlist, F., Covitz, P., Shanbhag, K., Foster, I., Saltz, J.: caGrid 1.0: An Enterprise Grid Infrastructure for Biomedical Research. Journal of the American Medical Informatics Association (JAMIA) 15, 138–149 (2008)
Felten, C.L., Strauss, J.S., Okada, D.H., Marchevsky, A.: Virtual microscopy: high resolution digital photomicrography as a tool for light microscopy simulation. Hum. Pathol. 30(4), 477–483 (1999)
Singson, R.P., Natarajan, S., Greenson, J.K., Marchevsky, A.: Virtual microscopy and the Internet as telepathology consultation tools. A study of gastrointestinal biopsy specimens. Am J. Clin. Pathol. 111(6), 792–795 (1999)
Ramirez, N.C., Barr, T.J., Billiter, D.M.: Utilizing virtual microscopy for quality control review. Dis Markers 23(5-6), 459–466 (2007)
Okada, D.H., Binder, S.W., Felten, C.L., Strauss, J.S., Marchevsky, A.M.: Virtual microscopy and the internet as telepathology consultation tools: Diagnostic accuracy in evaluating melanocytic skin lesions. Am J. Dermatopatholology 21(6), 525–531 (1999)
Afework, A., Beynon, M., Bustamante, F., et al.: Digital dynamic telepathology - the Virtual Microscope. In: The AMIA Annual Fall Symposium. American Medical Informatics Association (November 1998)
Goldberg, I., Allan, C., Burel, J.M., et al.: The open microscopy environment (OME) data model and xml file: Open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 6(R47) (2005)
Martone, M., Tran, J., Wong, W., Sargis, J., Fong, L., Larson, S., Lamont, S., Gupta, A., Ellisman, M.: The cell centered database project: An update on building community resources for managing and sharing 3d imaging data. Journal of Structural Biology 161(3), 220–231 (2008)
Manolakos, E., Funk, A.: Rapid prototyping of component-based distributed image processing applications using JavaPorts. In: Workshop on Computer-Aided Medical Image Analysis, CenSSIS Research and Industrial Collaboration Conference (2002)
Oberhuber, M.: Distributed high-performance image processing on the internet, MS Thesis, Technische Universitat Graz (2002)
Ludascher, B., Altintas, I., et al.: Scientific workflow management and the kepler system. Research articles. Concurr. Comput.: Pract. Exper. 18(10), 1039–1065 (2006)
Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Patil, S., Su, M.-H., Vahi, K., Livny, M.: Pegasus: Mapping Scientific Workflows onto the Grid. In: Dikaiakos, M.D. (ed.) AxGrids 2004. LNCS, vol. 3165, pp. 11–20. Springer, Heidelberg (2004)
Glatard, T., Montagnat, J., Pennec, X.: Efficient services composition for grid-enabled data-intensive applications. In: Proceedings of the IEEE International Symposium on High Performance Distributed Computing (HPDC 2006), Paris, France, June 19 (2006)
Andrade, H., Gedik, B., Wu, K., Yu, P.: Scale-Up Strategies for Processing High-Rate Data Streams in System S. In: The 25th International Conference on Data Engineering (ICDE 2009), Shangai, China, pp. 1375–1378 (2009)
Cudre-Mauroux, P., Lim, H., Simakov, J., et al.: A Demonstration of SciDB: A Science-Oriented DBMS. In: 35th International Conference on Very Large Data Bases (VLDB 2009), Lyon, France (2009)
Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: 6th Symposium on Operating Systems Design and Implementation (OSDI 2004), San Francisco, CA (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Widener, P.M. et al. (2012). High Performance Computing Techniques for Scaling Image Analysis Workflows. In: Jónasson, K. (eds) Applied Parallel and Scientific Computing. PARA 2010. Lecture Notes in Computer Science, vol 7134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28145-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-28145-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28144-0
Online ISBN: 978-3-642-28145-7
eBook Packages: Computer ScienceComputer Science (R0)