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

An Adaptive Multi-threshold Image Segmentation Method without Preassigning the Number of Segmented Regions

为解决多阈值图像分割中分割区域数较难确定的问题,提出一种基于可逆跳跃马尔可夫链蒙特卡罗(RJMCMC)的自适应多阈值图像分割方法.基于图像直方图的多阈值分割的本质是寻找直方图各峰间的谷底,但其个数较难确定且各局部峰并非都是高斯分布.因此文中用更具普适性的混合α稳定分布拟合直方图,建立包含局部峰个数及各分布元参数的分层贝叶斯概率模型.采用RJMCMC后验概率推理自适应确定混合α稳定分布的分布元个数及各自参数,从而获得分割区域数和多阈值参数.针对单晶炉拉晶图像、人脑核磁共振图像及国际标准测试图进行测试,结果表明该方法准确地建立图像分割模型,得到满意的多阈值分割结果.

To solve the problem that it is difficult to choose the number of segmentation regions for muhi-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 a-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 a-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 standardtest images show that the image segmentation model is accurately constructed by the proposed method, and multi-threshold segmentation results of images are satisfactory.