A Nonlinear Adaptive Level Set for Image Segmentation
A Nonlinear Adaptive Level Set for Image Segmentation
复制标题
图像分割的非线性自适应水平集
DOI：
10.1109/tcyb.2013.2256891
复制
发表时间：
2014-03-01
影响因子：
11.8
通讯作者：
Li, Xuelong
中科院分区：
文献类型：
--
作者：
Wang, Bin;Gao, Xinbo;Li, Xuelong
本文提出了一种新的图像分割水平集方法（LSM）。利用贝叶斯规则，我们设计了一个非线性自适应速度和概率加权停止力，实现了一个强大的分割对象的弱边界。该方法具有以下三个特点：1）利用贝叶斯规则自动确定曲线的收缩或膨胀，以涉及图像的区域特征; 2）驱动曲线以适当的速度演化，以避免弱边界处的泄漏; 3）减少虚假边界的影响，即，边缘远离感兴趣的对象。我们将所提出的分割方法应用于人工图像，医学图像和BSD-300图像数据集的定性和定量评价。比较结果表明，所提出的方法执行竞争力，与最小二乘及其代表性的变体相比。
In this paper, we present a novel level set method (LSM) 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 for objects with weak boundaries. The proposed method is featured by the following three properties: 1) it automatically determines the curve to shrink or expand by utilizing the Bayesian rule to involve the regional features of images; 2) it drives the curve evolve with an appropriate speed to avoid the leakage at weak boundaries; and 3) 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, medical images and the BSD-300 image dataset for qualitative and quantitative evaluations. The comparison results show the proposed method performs competitively, compared with the LSM and its representative variants.