Abstract: As Local Image Fitting (LIF) model is sensitive to the location of initial curve and can be easily trapped into local minimums, an active contour model based on image region information is proposed. This model uses global and local image information to segment images. Its energy function consists of four terms: local term, area term, length term and penalty term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. The introduction of a global indicating function in the area term can speed up the convergence of the proposed model and avoid being trapped into local minima. As the constrained level-set function of the penalty term approaches the signed distance function, the proposed model does not need re-initialization, thus the segmentation time is reduced. In addition, to segment the interested region of an image, narrow-band realization method is given for the proposed model. Experiment results show that, the proposed model is insensitive to the initial contour; its convergent speed is fast; it can accurately segment images with uneven gray intensity, and can segment the interested region of an image.

Key words: computer application, image segmentation, active contour model, level set method, intensity inhomogeneity