A novel algorithm of level set image segmentation based on bias field correction C-V model

A real-world image often has intensity inhomogeneity, which severely challenges image segmentation. Currently, many image segmentation algorithms assume that the intensity distribution is homogeneous, and the assumption badly affects the precision of their real-world image segmentation. Therefore, using the C-V model, we propose what we believe to be a novel algorithm of level set image segmentation, which eliminates the intensity inhomogeneity by correcting the bias field to suppress the intensity inhomogeneity. To verify the performance of our algorithm, we simulate the segmentation of both real-world images and artificial images. The simulation results, given in Figs.1 through 3 show that our algorithm has better segmentation precision than that of the tradition C-V model, can effective suppress the intensity inhomogeneity and noise interference and segment both the real-world image and the artificial image.