A level set image segmentation method based on a cloud model as the priori contour

Abstract

A novel image segmentation method combining a cloud model and a level set (CM-LS) is proposed in this article. At present, the cloud model can only obtain the rough segmentation result of an image, but the level set method is sensitive to the initial contour. The core idea of this method is to use the rough segmentation result of cloud model as the initial contour of the level set and then obtain the final result by the contour evolution. In this method, the cloud model is used to decompose the boundary of the image, which reduces the occurrence probability and occurrence degree of the instability problem caused by artificial intervention; at the same time, the convergence of the level set function is accelerated, and the initializing operation of the level set function that uses the cloud model algorithm can also effectively reduce the noise sensitivity of the function itself. Compared with the conventional level set method, the proposed method is general and accurate. The experimental data set in this article includes natural images of the Berkeley database, medical images and synthetic noise images. The experimental results show that the method is effective.