In this paper, we present a novel region-based active contour model based on global in-tensity fitting energy in a variational level set framework. Meanwhile, an internal energy term is in-troduced, and it forces the level set function to be close to a signed distance function. Image global information utilized efficiently makes the proposed model insensitive to noise, and the introduced penalty term can avoid the costly re-initialization for the evolving level set function, which not only speeds up the contour evolvement, but also improves accuracy of the final contour. Comparisons with other classical region-based models, such as Chan-Vese model and Region-Scalable Fitting (RSF) model, show the advantages of our model in terms of efficiency and accuracy. Moreover, the model is robust to noise.
