  In this paper, we present new image segmentation methods based on hidden
Markov random fields (HMRFs) and cuckoo search (CS) variants. HMRFs model the
segmentation problem as a minimization of an energy function. CS algorithm is
one of the recent powerful optimization techniques. Therefore, five variants of
the CS algorithm are used to compute a solution. Through tests, we conduct a
study to choose the CS variant with parameters that give good results
(execution time and quality of segmentation). CS variants are evaluated and
compared with non-destructive testing (NDT) images using a misclassification
error (ME) criterion.
