The Journal of Supercomputing

, Volume 73, Issue 5, pp 1929–1951 | Cite as

Accelerating compute-intensive image segmentation algorithms using GPUs

  • Mohammed Shehab
  • Mahmoud Al-Ayyoub
  • Yaser Jararweh
  • Moath Jarrah


Image segmentation is an important process that facilitates image analysis such as in object detection. Because of its importance, many different algorithms were proposed in the last decade to enhance image segmentation techniques. Clustering algorithms are among the most popular in image segmentation. The proposed algorithms differ in their accuracy and computational efficiency. This paper studies the most famous and new clustering algorithms and provides an analysis on their feasibility for parallel implementation. We have studied four algorithms which are: fuzzy C-mean, type-2 fuzzy C-mean, interval type-2 fuzzy C-mean, and modified interval type-2 fuzzy C-mean. We have implemented them in a sequential (CPU only) and a parallel hybrid CPU–GPU version. Speedup gains of 6\(\times \) to 20\(\times \) were achieved in the parallel implementation over the sequential implementation. We detail in this paper our discoveries on the portions of the algorithms that are highly parallel so as to help the image processing community, especially if these algorithms are to be used in real-time processing where efficient computation is critical.


Image segmentation GPUs Performance evaluation Fuzzy clustering algorithms 



This work is funded by Jordan University of Science and Technology (JO) (Grant No. 20160081).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceJordan University of Science and TechnologyIrbidJordan
  2. 2.Department of Computer EngineeringJordan University of Science and TechnologyIrbidJordan

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