Efficient active contour model driven by statistical and gradient information
结合统计和梯度信息的高效活动轮廓模型

A novel active contour model driven by statistical and gradient information is proposed in this paper. The model not only efficiently utilizes the gradient information of an object, which is in favor of fast and accurate location of boundaries, but also makes full use of the statistical information, including the global and local region information, which makes our method robust to noise. The use of the local region information makes the method free from intensity inhomogeneity of images, and the use of the global information helps to avoid the evolved contour trapping into local minima. Therefore, the initial contour can be set anywhere. Finally, the level set function is regularized by a Gaussian convolution kernel, which avoids an expensive computational re-initialization or regularization of the conventional models. Experimental results show that the proposed method can accurately and efficiently segment the homogenous images, as well as the inhomogenous images, with the initial contour set anywhere. Furthermore, the model is robust to noise.