Wavelet Domain Possibilistic C-Means Clustering Based on Markov Random Field for Image Segmentation
In this paper, an unsupervised multiresolution image segmentation technique is presented, which combines wavelet domain Markov random field with possibilistic c-means clustering algorithm. At the determination of wavelet coefficients likelihood model stage, Gaussian mixture model is used to characterize the wavelet coefficients statistical distribution, and the model parameters are estimated by expectation maximization algorithm. In order to capture the clustering property of wavelet coefficients, we establish the prior probability model of label field, according to maximum a posterior rule, the optimum conditional probability likelihood model of wavelet coefficients given the labels is determined. At the image segmentation stage, we establish possibilistic cmeans clustering objective function based on the conditional probability likelihood model of wavelet coefficients. In order to capture the clustering property of wavelet coefficients, we incorporate the local statistical distribution of wavelet coefficients into the clustering objective function. The improved objective function with spatial constraints is optimizated, we can get a new image segmentation algorithm. The simulation on magnetic resonance image shows that the new multiresolution image segmentation technique obtains much better segmentation results, such as the accuracy of boundary localization and the correctness of distinguishing different tissues.
wavelet transform Markov random field Image segmentation possibilistic c-means multiresolution
Xuchao Li Lihua Yan
College of Information Science and Media Jinggangshan University Jian,China Department of Computer Science and Technology Chifeng University Chifeng,China
国际会议
上海
英文
2723-2727
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）