The performances of iterative type-2 fuzzy C-mean on GPU for image segmentation

Abstract

Fuzzy C-mean (FCM) is an algorithm for data segmentation and classification, robust and very popular within the scientific community. It is used in several fields such as computer vision, medical imaging and remote control. The purpose of this paper is to propose a parallel implementation of the iterative type-2 fuzzy C-mean (IT2FCM) algorithm on a massively parallel SIMD architecture to segment different MRI images. IT2FCM is an FCM standard variant; its objective is identical to that of FCM, except that the first has a higher accuracy level than the second. However, it is expensive in terms of time processing. Therefore, it is practically important to reduce its execution time while preserving the quality of the segmentation. This implementation is then compared with the sequential versions in the C language and Python using the Numpy and Numba libraries, and then, we compared it to another parallel method from the literature. The execution time obtained is faster than the sequential versions by about 15 × and 4 × for the second parallel version. The results achieved are very satisfactory compared to those taken from the literature.

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Correspondence to Noureddine Ait Ali.

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Ali, N.A., abbassi, A.E. & Cherradi, B. The performances of iterative type-2 fuzzy C-mean on GPU for image segmentation. J Supercomput (2021). https://doi.org/10.1007/s11227-021-03928-9

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Keywords

  • Fuzzy C-mean
  • IT2FCM
  • SIMD
  • Numba
  • Python
  • Segmentation
  • Medical imaging