An adaptive multi-threshold image segmentation method without preassigning the number of segmented regions

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.