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The correction of images corrupted by bias field artefact is still challenging task both at accuracy level as on the computational plane. The work in this paper focus on the second constraint by giving mathematical models of experimental execution time per iteration ETPI(s) on GPU and CPU implementations and speed-ups GPU/CPU(x) of the iterative Bias Field Correction Fuzzy C-means clustering Algorithm (Both sequential BCFCM and parallel PBCFCM versions) against the variable cluster number. In this study we characterize the behaviour of these devices against the proposed implementations to show the optimal situation that offers the best performance. The modelling results of cluster number variation show interesting behaviours and are in good accordance to experimental ones. 424 Bouchaib Cherradi et al.

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