基于改进符号压力函数的变分水平集图像分割算法

为了更好地解决含有弱边界、灰度不均匀的图像在分割时出现的轮廓线错误移动而导致分割结果错误的问题,结合图像的统计信息,构造出一种新的符号压力(SPF)函数,提出了一种基于改进的压力符号函数的变分水平集图像分割算法。首先,利用新的压力符号函数代替边缘函数,构造了新的活动轮廓模型；其次,该算法保持了测地线活动轮廓(GAC)模型和chan-vese(C-V) 模型的优点,使水平集函数演化到目标的边界上；最后,对一些弱边界、灰度不均匀的图像进行仿真实验,结果表明提出的算法能够精准地分割目标,并且具有一定的抗噪性。

Variational Level Set Method for Image Segmentation Based on Improved Signed Pressure Force Function

In order to handle the problem of inaccurate moving of contour which results in the wrong segmentation of the image with weak boundary and intensity inhomogeneity,a variational level set method for image segmentation based on improved signed pressure force function combined with the statistical information of image was proposed in this paper.Firstly,a new model of active contours is constructed by using a new pressure sign function to replace the edge function.Secondly,the algorithm maintains the merits of geodesic active contour(GAC) model and chan-vese(C-V) model and makes the level set function stop evolution in the boundary of the target image.Finally,simulation experiments were implemented on images with poor boundaries and intensity inhomogeneity.Experimental results show the proposed model has high computational efficiency and accuracy.Furthermore,it is robust to noise.