摘要
提出一种由统计和梯度信息驱动的活动轮廓模型。该模型有效利用梯度信息使演化轮廓线快速精确地定位到物体的边缘;同时,由局部统计信息和全局统计信息构造符号压力函数,减少噪声对轮廓线演化的影响。另外,模型利用局部统计信息能够有效处理灰度分布不均的图像,全局信息的利用避免了演化轮廓线陷入局部最小,因此,该模型可以任意设置初始轮廓线。最后通过高斯卷积核对水平集函数规则化,避免了传统模型中计算代价高昂的重新初始化和规则化。实验结果表明,提出的模型不仅能够在任意初始轮廓下精确有效地分割灰度分布均匀的图像和不均匀的图像,而且对噪声具有较好的鲁棒性。
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.
出处
《中国图象图形学报》
CSCD
北大核心
2011年第8期1489-1496,共8页
Journal of Image and Graphics
基金
国家自然科学基金重点项目(60736008)
国家自然科学基金项目(61003134,60872127)
北京市自然科学基金重点项目(4081002)
关键词
活动轮廓模型
统计和梯度信息
灰度分布不均匀
高斯卷积
初始轮廓线
active contour model
statistical and gradient information
intensity inhomogeneity
Gaussian convolution
initial contour
