基于参数活动轮廓模型的图像分割新方法

New method for image segmentation based on parametric active contour model

针对目前基于参数活动轮廓模型(PACM)的图像分割方法不能精确定位到角点,不连续边缘易受周围无关信息影响的缺陷,提出一种基于参数活动轮廓模型的图像分割新方法。该方法首先构造边缘保护项,将其引入到图像分割的活动轮廓模型中,保留拉普拉斯扩散项的切线方向分量;再引入两个权重参数控制切线方向和法线方向有偏的扩散,以提高分割的精度和效率。实验结果表明,该模型不仅能检测到弱边缘,精确定位到角点,而且能收敛到深度的凹形边界,降低无关信息对边缘不连续处的影响,防止边缘泄露,很好地保护图像细节,收敛的效率和准确率比边缘保护梯度向量流模型、法向梯度向量流模型及其改进模型有明显提高。

Aiming at the defects that the existing methods based on Parametric Active Contour Models（ PACM） cannot accurately locate to corners,and discontinuous edges were easily affected by the surrounding irrelevant information,a new method for image segmentation based on PACM was proposed. In this method,the edge preserving term was first constructed,which was introduced to active contour model of image segmentation,and the tangent direction of Laplace diffusion term still persisted,and then two weight parameters were introduced to control tangential direction and normal direction so that the accuracy and efficiency for segmentation were improved. Experimental results show that the proposed model can detect weak edges and locate accurately corners,meanwhile converges to the depth of concave boundary and reduce the impact of independent information on edge discontinuities. Furthermore,it overcomes the edge leakage and is very good for protecting image details. Both the efficiency and accuracy of segmentation are significantly improved in contrast with the edge preserving gradient vector flow models,the normalized gradient vector flow models and their improved models.