Otsu's thresholding method is widely applied in image segmentation

but it is not suitable to segment the images that have large difference in intra-class variance of object and background

this paper presents a regularization Otsu's segmentation method based on the information entropy of posterior probability of object and background obtained by thresholding image.From the point of view that image segmentation is essentially pixel clustering problem

the classical Otsu's thresholding method is firstly interpreted as a kind of weighting hard C-means clustering algorithm.Secondly

considering that the clustering segmentation has typical ill-posedness

the object and background from raw image are obtained by segmentation and their posterior probability information entropy is taken as the constraint item

the regularized modification of Otsu's thresholding criteria function is realized

and regularization Otsu's thresholding method is obtained.In the end

the reasonability of the proposed thresholding method is explained by mathematical analysis

and the selection method of its regularization parameter is put forward.Experimental results show that the proposed regularization Otsu's thresholding method is effective

and the traditional Ostu's thresholding method can be viewed as a special case of the proposed method.