Active Contours driven by Region-Scalable Fitting and Optimized Laplacian of Gaussian Energy for Image Segmentation


It had been known that the famous region scalable-fitting model can segment the images with intensity inhomogeneity effectively, but it largely depends on the position of initial contour. In this paper, an active contour model which combines region-scalable fitting energy and optimized Laplacian of Gaussian (LoG) energy is proposed for image segmentation. We first present a LoG energy term optimized by an energy functional which can smooth the homogeneous regions and enhance edge information at the same time. Then, we integrate the optimized LoG energy term with the region-scalable fitting energy term which makes use of local region information to drive the curve towards the boundaries. With the addition of LoG term, the proposed model is insensitive to the positions of initial contour and realizes an accurate segmentation result. Experiments on some synthetic and real images have proved that the proposed model not only has a good robustness of initialization, but also has a higher segmentation accuracy and efficiency than other major region-based models.