A Robust Soft Decision Mixture Model for Image Segmentation

In this paper, we present a novel soft decision mixture model for image segmentation. This model adopts the soft decision classify into gaussian mixture model to represent the probability distribution of the observed image feature. The model for the underlying true context images is designed to serve as prior contextual constraints on unobserved pixel labels in term of markov random field model. Experiments with synthetic image and real image show that the use of soft decision mixture model definitely improves the quality of the segmentation results for noisy images and results in reduced classification errors in the interior area of the region.

[1]  Julian Besag,et al.  Digital Image Processing: Towards Bayesian image analysis , 1989 .

[2]  H. B. Mitchell Markov Random Fields , 1982 .

[3]  H. Donald Gage,et al.  Statistical models of partial volume effect , 1995, IEEE Trans. Image Process..

[4]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Julian Besag,et al.  Towards Bayesian image analysis , 1993 .

[6]  Isak Gath,et al.  Fuzzy clustering for the estimation of the parameters of the components of mixtures of normal distributions , 1989, Pattern Recognit. Lett..

[7]  D R Haynor,et al.  Partial volume tissue classification of multichannel magnetic resonance images-a mixel model. , 1991, IEEE transactions on medical imaging.

[8]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  William D. Penny,et al.  Bayesian Approaches to Gaussian Mixture Modeling , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.