Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. Up to present, different approacheshave been proposed for segmentation of texture images. In this paper, we offeredunsupervised texture image segmentation based on Markov Random Field (MRF)model. First, we used Gabor filter with different parameters’ (frequency,orientation) values. The output image of this step clarified different textures andthen used low pass Gaussian filter for smoothing the image. These two filters wereused as preprocessing stage of texture images. In this research, we used K-meansalgorithm for initial segmentation. In this study, we used Expectation Maximization(EM) algorithm to estimate parameters, too. Finally, the segmentation was done byIterated Conditional Modes (ICM) algorithm updating the labels and minimizing theenergy function. In order to test the segmentation performance, some of the standardimages of Brodatz database are used. The experimental results show theeffectiveness of the proposed method.

Keywords:

EM algorithm , Image segmentation , Markov Random Field (MRF) , Texture image