A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region growing: a new approach
IEEE Transactions on Image Processing
Performance evaluation of finite normal mixture model-based image segmentation techniques
IEEE Transactions on Image Processing
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On the basis of Markov Random Field (MRF), which uses context information, in this paper, a robust image segmentation method is proposed. The relationship between observed pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function, which described the probability of pixels being classified into one class. To perform an unsupervised segmentation, the Bayes Information Criterion (BIC) is used to determine the class number. The K-means is employed to initialise the classification and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori (MAP) procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed method is adopted with K-means, traditional Expectation-Maximization (EM) and MRF image segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results and the histogram of signal to noise ratio (SNR)-miss classification ratio (MCR) showed that the proposed algorithm is the better choice.