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High-Throughput-Screening of Medical Image Data on Heterogeneous Clusters

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Large-Scale Scientific Computing (LSSC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7116))

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Abstract

Non-invasive medical imaging by means of computed tomography (CT) and fMRI helps clinicians to improve diagnostics and - hopefully - treatment of patients. Due to better image resolutions as well as ever increasing numbers of patients who undergo these procedures, the amount of data that have to be analyzed puts great strain on radiologists. In an ongoing development with SALK (Salzburger Landeskrankenhaus) we propose a system for automated screening of CT data for cysts in the patient’s kidney area. The proper detection of kidneys is non-trivial, due the high variance of possible size, location, levels of contrast and possible pathological anomalies a human kidney can expose in a CT slice. We employ large-scale, semi-automatically generated dictionaries (based on 107 training images) to be used in injunction with principal component analysis (PCA). Heterogeneous clusters of CPU-, GPGPU-, and Cell BE-processors are used for high-throughput-screening of CT data. For data-parallel programming CUDA, OpenCL and the IBM Cell SDK have been used. Task parallelism is based on OpenMPI and a dynamic load-balancing scheme, which demonstrates very low latencies by means of double-buffered, multi-threaded queues.

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References

  1. Ghosh, P., Mitchell, M.: Segmentation of medical images using a genetic algorithm. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 1171–1178. ACM, New York (2006)

    Chapter  Google Scholar 

  2. Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)

    MATH  Google Scholar 

  3. Lai, C.C., Chang, C.Y.: A hierarchical evolutionary algorithm for automatic medical image segmentation. Expert Syst. Appl. 36(1), 248–259 (2009)

    Article  Google Scholar 

  4. Petkov, N.: Biologically motivated computationally intensive approaches to image pattern recognition. Future Generation Computer Systems 11(4-5), 451–465 (1995)

    Article  Google Scholar 

  5. Pinto, N., Doukhan, D., DiCarlo, J.J., Cox, D.D.: A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Comput. Biol. 5(11), e1000579 (2009), http://dx.doi.org/10.1371%2Fjournal.pcbi.1000579

    Article  MathSciNet  Google Scholar 

  6. Johnson, M., Shotton, J.: Semantic Texton Forests. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds.) Computer Vision. SCI, vol. 285, pp. 173–203. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Touhami, W., Boukerroui, D., Cocquerez, J.P.: Fully Automatic Kidneys Detection in 2D CT Images: A Statistical Approach. In: Duncan, J., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 262–269. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991), http://dx.doi.org/10.1162/jocn.1991.3.1.71

    Article  Google Scholar 

  9. Weber, R.: Similarity Search in High-Dimensional Data Spaces. Ph.D. thesis, Grundlagen von Datenbanken (1998)

    Google Scholar 

  10. Zinterhof, P.: Distributed Computation of Feature-Detectors for Medical Image Processing on GPGPU and Cell Processors. In: Guarracino, M.R., Vivien, F., Träff, J.L., Cannatoro, M., Danelutto, M., Hast, A., Perla, F., Knüpfer, A., Di Martino, B., Alexander, M. (eds.) Euro-Par-Workshop 2010. LNCS, vol. 6586, pp. 339–347. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Salzburg University, Austria

    Peter Zinterhof

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  1. Peter Zinterhof
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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev, Block 25A, 1113, Sofia, Bulgaria

    Ivan Lirkov  & Svetozar Margenov  & 

  2. Department of Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, Building 321,, 2800, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Zinterhof, P. (2012). High-Throughput-Screening of Medical Image Data on Heterogeneous Clusters. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2011. Lecture Notes in Computer Science, vol 7116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29843-1_42

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  • DOI: https://doi.org/10.1007/978-3-642-29843-1_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29842-4

  • Online ISBN: 978-3-642-29843-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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