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 ...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.展开更多
基金Supported by the State Key Program of National Natural Science of China (No. 61003134, 60736008)the National Natural Science Foundation of China (No. 60803082)the Key Program of Natural Science of Beijing (No.4081002)
文摘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.
