Kilic I.Kayacan O.2024-07-222024-07-22201203784371http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/17574In this paper, the iterated conditional modes optimization method of a Markov random field technique for image segmentation is generalized based on Tsallis statistics. It is observed that, for some q entropic index values the new algorithm performs better segmentation than the classical one. The proposed algorithm also does not have a local minimum problem and reaches a global minimum energy point although the number of iterations remains the same as ICM. Based on the findings of the new algorithm, it can be expressed that the new technique can be used for the image segmentation processes in which the objects are Gaussian or nearly Gaussian distributed. © 2012 Elsevier B.V. All rights reserved.EnglishGlobal optimizationMarkov processesGaussian distributedImage segmentation processIterated conditional modesLocal minimum problemMarkov Random FieldsNumber of iterationsOptimization methodTsallis entropiesImage segmentationArticle10.1016/j.physa.2011.12.062