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In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation. Instead of using model parameters as texture features, we exploit the variations in parameter estimates found by model fitting in local region around the given pixel. The spatially localized estimation process is carried out by maximum likelihood method employing a moderately small estimation window which leads to modeling of partial texture characteristics belonging to the local region. Hence significant fluctuations occur in the estimates which can be related to texture pattern complexity. The variations occurred in estimates are quantified by normalized local histograms. Selection of an accurate window size for histogram calculation is crucial and is achieved by a technique based on the entropy of textures. These texture features expand the possibility of using relatively low order GMRF model parameters for segmenting fine to very large texture patterns and offer lower computational cost. Small estimation windows result in better boundary localization. Unsupervised segmentation is performed by integrated active contours, combining the region and boundary information. Experimental results on statistical and structural component textures show improved discriminative ability of the features compared to some recent algorithms in the literature.

Abstract:A novel texture feature based on isotropic Gaussian Markov random fields is proposed for diagnosis and quantification of emphysema and its subtypes. Spatially varying parameters of isotropic Gaussian Markov random fields are estimated and their local distributions constructed using normalized histograms are used as effective texture features. These features integrate the essence of both statistical and structural properties of the texture. Isotropic Gaussian Markov Random Field parameter estimation is computationally efficient than the methods using other MRF models and is suitable for classification of emphysema and its subtypes. Results show that the novel texture features can perform well in discriminating different lung tissues, giving comparative results with the current state of the art texture based emphysema quantification. Furthermore supervised lung parenchyma tissue segmentation is carried out and the effective pathology extents and successful tissue quantification are achieved.

Abstract:In statistical model based texture feature extraction, features based on spatially varying parameters achieve higher discriminative performances compared to spatially constant parameters. In this paper we formulate a novel Bayesian framework which achieves texture characterization by spatially varying parameters based on Gaussian Markov random fields. The parameter estimation is carried out by Metropolis-Hastings algorithm.The distributions of estimated spatially varying parameters are then used as successful discriminant texture features in classification and segmentation. Results show that novel features outperform traditional Gaussian Markov random field texture features which use spatially constant parameters. These features capture both pixel spatial dependencies and structural properties of a texture giving improved texture features for effective texture classification and segmentation.