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Image segmentation is the process of partitioning the image into significantregions easier to analyze. Nowadays, segmentation has become a necessity inmany practical medical imaging methods as locating tumors and diseases. HiddenMarkov Random Field model is one of several techniques used in imagesegmentation. It provides an elegant way to model the segmentation process.This modeling leads to the minimization of an objective function. ConjugateGradient algorithm (CG) is one of the best known optimization techniques. Thispaper proposes the use of the Conjugate Gradient algorithm (CG) for imagesegmentation, based on the Hidden Markov Random Field. Since derivatives arenot available for this expression, finite differences are used in the CGalgorithm to approximate the first derivative. The approach is evaluated usinga number of publicly available images, where ground truth is known. The DiceCoefficient is used as an objective criterion to measure the quality ofsegmentation. The results show that the proposed CG approach compares favorablywith other variants of Hidden Markov Random Field segmentation algorithms.

Combination of Hidden Markov Random Field and Conjugate Gradient for Brain Image Segmentation