Earticle

The image segmentation result based on level set typically depends on the appropriate manual initial contour. In this paper, we introduce an autonomous approach for deciding the initial level set contour to be close to the actual boundary as far as possible, and the decided initial contour can be directly evolved by various level set methods. Such an improvement can speed up the evolution and lead to a more robust segmentation result. Then, we consider the statistical information of three distinct regions to construct a new level set model, including contour, contour inside and outside. Combining the two steps above is helpful to obtain a pretty ideal segmentation effect. Some remarkable results and shorter execution time for some difficult segmentation tasks shown in this paper demonstrate the potential of our innovative approach.