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In this paper we discuss texture classification of microstructural images used in the development of advanced materials. Different materials or different phases of a material in a sample lead to different textures in images of that sample. We investigate the application of the expectation-maximization/ maximization of the posterior marginal (EM/MPM) algorithm proposed in  for automated texture-based segmentation of these microstructure images. We propose a new method to initialize the parameters used for the observed image under a Gaussian model. In addition, we examine the use of 8-nearest- neighborhood system for segmentation to locate objects edges more precisely. The experimental results demonstrate the adaptability of the EM/MPM algorithm with appropriate choices of model parameters and neighborhood system. Furthermore, we demonstrate that the use of 8-nearest-neighborhood system can provide smoother segmentation in edges.