Abstract: Level set method has been widely used in image segmentation. Edge-based active contour model mainly drives the curve to the target boundary by gradient information, but the model based on gradient information makes the segmentation produce over-segmentation. And for the uneven gray image, the processing results are not so satisfactory.\linebreak However, the region-based active contour model controls the curves evolution through regional
information, which will get the segmentation results based on the whole image. For the reasons stated above new regional component is put forward in the Level set, by utilizing the square of mean gray value difference between the background and the target area in energy function, a new improved method for image segmentation is proposed. Compared with the general active contour model, the experimental results show that the active contour model~with the
regional difference information has ideal effect of segmentation,~faster evolution speed and higher efficiency. We can get more satisfactory segmentation results.

Key words: inter-region dissimilarity, level set method, adaptive evolution coefficient, geometric active contour model