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Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations

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Abstract

Medical image processing is one of the most famous image processing fields in this era. This fame comes because of the big revolution in information technology that is used to diagnose many illnesses and saves patients lives. There are many image processing techniques used in this field, such as image reconstructing, image segmentation and many more. Image segmentation is a mandatory step in many image processing based diagnosis procedures. Many segmentation algorithms use clustering approach. In this paper, we focus on Fuzzy C-Means based segmentation algorithms because of the segmentation accuracy they provide. In many cases, these algorithms need long execution times. In this paper, we accelerate the execution time of these algorithms using Graphics Process Unit (GPU) capabilities. We achieve performance enhancement by up to 8.9x without compromising the segmentation accuracy.

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Notes

  1. https://people.eecs.berkeley.edu/~sangjin/2013/02/12/CPU-GPU-comparison.html

  2. https://msdn.microsoft.com/en-us/library/z9z62c29.aspx

  3. http://www.c-sharpcorner.com/uploadfile/rafaelwo/cuda-integration-with-C-Sharp/

  4. https://central.xnat.org/REST/experiments/

  5. http://www.mammoimage.org/databases/

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Acknowledgments

This work is supported by the Jordan University of Science and Technology Deanship of Research project number 20150310.

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Authors and Affiliations

  1. Departement of Computer Science, Jordan University of Science and Technology, Irbid, Jordan

    Mohammad A. Alsmirat, Yaser Jararweh, Mahmoud Al-Ayyoub & Mohammed A. Shehab

  2. National Institute of Technology Kurukshetra, Haryana, India

    Brij B. Gupta

Authors
  1. Mohammad A. Alsmirat
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  2. Yaser Jararweh
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  3. Mahmoud Al-Ayyoub
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  4. Mohammed A. Shehab
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  5. Brij B. Gupta
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Correspondence to Mohammad A. Alsmirat.

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Alsmirat, M.A., Jararweh, Y., Al-Ayyoub, M. et al. Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations. Multimed Tools Appl 76, 3537–3555 (2017). https://doi.org/10.1007/s11042-016-3884-2

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  • DOI: https://doi.org/10.1007/s11042-016-3884-2

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