一种无须预指定分割区域数的自适应多阈值图像分割方法<sup>*</sup>

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Abstract To solve the problem that it is difficult to choose the number of segmentation regions for multi-threshold image segmentation, an adaptive multi-threshold image segmentation method based on Reversible Jump Markov Chain Monte Carlo (RJMCMC) method is proposed. Histogram-based image segmentation is essential to search the bottom between peaks. However, the multi-threshold segmentation number is difficult to determine and not all local peaks follow Gaussian distribution. Therefore, mixture of α-stable distributions is adopted to fit image gray level histogram. Firstly, a hierarchical Bayesian probability model is established with the number of local peaks and the various parameters for each component. Then, posterior probability reasoning based on RJMCMC is implemented to adaptively obtain the best number of α-stable distribution function and the parameters for each distribution. The experimental results on the single crystal pulling image, the simulated magnetic resonance imaging (MRI) image and international standard test images show that the image segmentation model is accurately constructed by the proposed method, and multi-threshold segmentation results of images are satisfactory.

Key words： Mixture of α-stable Distributions Reversible Jump Markov Chain Monte Carlo (RJMCMC) Adaptive Multi-threshold Image Segmentation Bayesian Inference

Received: 10 February 2014

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	Articles by authors
	CHEN Ya-Jun
	LIU Ding
	LIANG Jun-Li
	ZHANG Xin-Yu

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CHEN Ya-Jun,LIU Ding,LIANG Jun-Li等. An Adaptive Multi-threshold Image Segmentation Method without Preassigning the Number of Segmented Regions[J]. , 2014, 27(11): 993-1004.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2014/V27/I11/993

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