Abstract
Window Memoization is a performance improvement technique for image processing algorithms. It is based on removing computational redundancy in an algorithm applied to a single image, which is inherited from data redundancy in the image. The technique employs a fuzzy reuse mechanism to eliminate unnecessary computations. This paper extends the window memoization technique such that in addition to exploiting the data redundancy in a single image, the data redundancy in a sequence of images of a volume data is also exploited. The detection of the additional data redundancy leads to higher speedups. The cascaded window memoization technique was applied to Canny edge detection algorithm where the volume data of prostate MR images were used. The typical speedup factor achieved by cascaded window memoization is 4.35x which is 0.93x higher than that of window memoization.
Keywords
- Fuzzy memoization
- Inter-frame redundancy
- Performance optimization
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References
Haas, B., et al.: Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies. Phys. Med. Biol. 53, 1751–1771 (2008)
Hodgea, A.C., et al.: Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D. Computer Methods and Programs in Biomedicine 84, 99–113 (2006)
Gubern-Merida, A., Marti, R.: Atlas based segmentation of the prostate in MR images. In: MICCAI: Segmentation Challenge Workshop (2009)
Intel Integrated Performance Primitives, http://software.intel.com/en-us/articles/intel-ipp/
NVIDIA, http://www.nvidia.com/
RapidMind, software.intel.com/en-us/articles/intel-array-building-blocks/
Hennessy, J.L., Patterson, D.A.: Computer Architecture - A quantitative approach, 4th edn. Morgan Kaufmann, San Francisco (2007)
Khalvati, F.: Computational Redundancy in Image Processing, Ph.D. thesis, University of Waterloo (2008)
Michie, D.: Memo functions and machine learning. Nature 218, 19–22 (1968)
Bird, R.S.: Tabulation techniques for recursive programs. ACM Computing Surveys 12(4), 403–417 (1980)
Pugh, W., Teitelbaum, T.: Incremental computation via function caching. In: ACM Symposium on Principles of Programming Languages, pp. 315–328 (1989)
Wang, W., Raghunathan, A., Jha, N.K.: Profiling driven computation reuse: An embedded software synthesis technique for energy and performance optimization. In: IEEE VLSID 2004 Design, p. 267 (2004)
Huang, J., Lilja, D.J.: Extending value reuse to basic blocks with compiler support. IEEE Transactions on Computers 49, 331–347 (2000)
Salami, E., Alvarez, C., Corbal, J., Valero, M.: On the potential of tolerant region reuse for multimedia applications. In: International Conference on Supercomputing, pp. 218–228 (2001)
Alvarez, C., Corbal, J., Valero, M.: Fuzzy memoization for floating-point multimedia applications. IEEE Transactions on Computers 54(7), 922–927 (2005)
Preiss, B.R.: Data Structures and Algorithms with Object-Oriented Design Patterns in C++. John Wiley and Sons, Chichester (1999)
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© 2011 IFIP International Federation for Information Processing
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Khalvati, F., Kianpour, M., Tizhoosh, H.R. (2011). Cascaded Window Memoization for Medical Imaging. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds) Artificial Intelligence Applications and Innovations. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23960-1_33
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DOI: https://doi.org/10.1007/978-3-642-23960-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23959-5
Online ISBN: 978-3-642-23960-1
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