Image Segmentation is a fundamental approach in the field of Image processing and based on user’ sapplication. This paper propose an original and simple Segmentation strategy based on the EM approach thatresolves many informatics problems about hyperspectral Images which are observed by airborne sensors. In afirst step, to simplify the input color textured Image into a color Image without texture. The final Segmentationis simply achieved by a spatially color Segmentation using feature vector with the set of color valuescontained around the pixel to be classified with some mathematical equations. The spatial constraint allowstaking into account the inherent spatial relationships of any Image and its color. This approach provideseffective PSNR for the segmented Image. These results have the better performance as the segmented Imagesare compared with Watershed & Region Growing Algorithm and provide effective Segmentation for theSpectral Images & Medical Images.

In this paper the method used for Image compression is based on successive Image Segmentation. The Image to be compressed is segmented into smaller regions and this process is continued until each region can be approximated by a desired parameter so that the error be small enough in each region. In general, each region is an n-gon n where 3 < n < 8 . This method has short coding and decoding times. The coder is tree-structured, thus it is suitable for progressive Image coding.

Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an Image. The parameters of the model were estimated by EM-algorithm.In addition pixel labeling corresponded to each pixel of true Image was made by Bayes rule. In fact, a new numerically method was introduced for finding the maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we were made a sequence of priors; posteriors were made and then converged to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability which can make labeled Image. This labeled Image shows our segmented Image with reduced noises. We presented this method in several experiments.

Texture Image analysis is one of the most important working realms of Image processing in medical sciences and industry. Up to present, different approaches have been proposed for Segmentation of texture Images. In this paper, we offered unsupervised 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 and then used low pass Gaussian filter for smoothing the Image. These two filters were used as preprocessing stage of texture Images. In this research, we used K-means algorithm for initial Segmentation. In this study, we used Expectation Maximization (EM) algorithm to estimate parameters, too. Finally, the Segmentation was done by Iterated Conditional Modes (ICM) algorithm updating the labels and minimizing the energy function. In order to test the Segmentation performance, some of the standard Images of Brodatz database are used. The experimental results show the effectiveness of the proposed method.