非线性自适应水平集的图像分割方法

A Nonlinear Adaptive Level Set for Image Segmentation

提出一种新的基于水平集的图像分割方法,通过引入贝叶斯规则,设计一个可有效分割弱边缘的非线性自适应速度和概率加权停止函数。该方法包含如下特性:可以自动决定曲线收缩或利用贝叶斯规则对涉及到的图像区域特征进行扩展;以恰当的速度驱动曲线演变,避免了弱边缘的遗漏;降低了假边缘的影响。最后将所提出的分割方法应用于人工图像、医学图像和自然图像的定性和定量评估。对结果进行比较可知,该方法相对于水平集方法和其代表的变体更加有效和实用。

In this paper, we present a novel level set method （I.SM） for image segmentation. By utilizing the Bayesian rule, we design a nonlinear adaptive velocity and a probability--weighted stopping force to implement a robust segmentation （or objects with weak boundaries. The proposed method is featured by the following three properties：it automatically deter mines the curve to shrink or expand by utilizing the Bayesian rule to involve the regional features of images;it drives the curve evolw＇, with an appropriate speed to avoid the leakage at weak boundaries; and it reduces the influence of false boundaries,i, e. ,edges far away from objects of interest. We applied the proposed segmentation method to artificial images,medieal images and natural images for qualitative and quantitative evaluations. The comparison resuhs show the proposed method performs competitively,compared with the LSM and its representative variants.