Active Contours Based on Image Laplacian Fitting Energy

The zero-crossings of image Laplacian isimportant for edge detection, which can accurately determine the edges of the image. In this paper, we propose anovel active contour model that utilizes the image Laplacian to construct an energy functional. We minimize thisfunctional and get a term which is related to typical image segmentation that the boundary is the zero-crossingsof image Laplacian. In order to improving the ability toresist noise and extending the capture range of the forcebased on this energy functional, we propose another energyfunctional of total variation for image Laplacian. Moreover, our model is incorporated with a variational levelset formulation without re-initialization proposed by Liet al. Therefore, re-initialization is unnecessary. In addition, interior contours are automatically detected withonly one initial contour. Comparisons with other majoredge-based or region-based models, such as Geodesic active contours (GAC) and the piecewise constant model (CV model), show advantages in segmentation of images withweak edges or intensity in-homogeneity.