Abstract：In order to maintain the stability of traditional level set methods,the re-initialization method or a signed distance function is often used. However,those two methods are either time-consuming or instable. Thus,a signed distance function based level set method is proposed for overcoming those disadvantages. Firstly,the existing Double-Well constraint term is improved,which avoids re-initialization,increases the computational efficiency and makes the evolution more stable. Secondly,the active contour model based on the global grey information and local grey information is used to construct the energy function,thus it inherits the advantages of global and local models and drives the level set function accurately to real objective boundaries. Besides,the weigh of combination can be adjusted dynamically. At last,Gaussian convolution is presented to accelerate the speed of evolution and smooth the level set function. The experiments on both synthesis images and real images show that the proposed method has high computational efficiency and accuracy,and it is robust to noise and initial contour.
崔玉玲. 基于改进符号距离函数的变分水平集图像分割算法[J]. 模式识别与人工智能, 2013, 26(11): 1033-1040.
CUI Yu-Ling. A Variational Level Set Method for Image Segmentation Based on Improved Signed Distance Function. , 2013, 26(11): 1033-1040.
2013, Vol. 26 
