Multimedia Tools and Applications

, Volume 76, Issue 3, pp 3537–3555 | Cite as

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

  • Mohammad A. AlsmiratEmail author
  • Yaser Jararweh
  • Mahmoud Al-Ayyoub
  • Mohammed A. Shehab
  • Brij B. Gupta


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.


Fuzzy C-Means Possibilistic C-Means CUDA Medical image processing Image segmentation 



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


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Departement of Computer ScienceJordan University of Science and TechnologyIrbidJordan
  2. 2.National Institute of Technology KurukshetraHaryanaIndia

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