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

摘要

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, poste-rior probability reasoning based on RJMCMC is implemented to adaptively obtain the best number ofα-sta-ble 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.%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。