C-V模型的一种集成化改进

C-V(Chan-Vese)模型不能分割灰度不均图像,分割速度慢.因此,对图像分割理论进行深入探讨,采用实验方法,提出一个新的边缘停止函数,并将其与图像灰度变换及H1正则项融入C-V模型中,对其进行集成化改进,提出一个新颖的以偏微分方程形式表示的活动轮廓模型.实验表明:该模型能分割灰度不均图像,对初始轮廓的大小敏感性较小,抗噪性较强,分割速度较快.     

基于改进Chan-Vese模型的电力设备红外图像分割

电力设备红外图像分割是电力设备模式识别和红外故障诊断的基础。Chan-Vese模型能够有效分割含强噪声和边缘模糊的图像,但其分割速度缓慢,并且在分割电力设备红外图像时不能有效消除无关背景。提出一种改进的Chan-Vese模型,采用多个初始轮廓,并采用二值函数代替距离函数初始化水平集函数;同时对Chan-Vese模型的梯度下降流提出改进,简化其图像数据项,并用一个高斯核函数取代长度正则项。改进的模型不仅方便计算,而且可以在迭代过程中采用更大时间步长,加快曲线演化速度。在对电力设备红外图像的分割实验中,证明了相比Chan-Vese模型,新模型分割速度明显提高,并且具备较好的消除无关背景的性能。     

曲率无关方向扩散及改进型Chan-Vese主动轮廓模型

低信噪比条件下的图像分割是图像分割与应用中所面临的难点之一.针对当前非参数化主动轮廓分割模型在低信噪比、高曲率条件下难以准确收敛到目标边缘的问题,以主动轮廓模型中的噪声处理项作为切入点,提出一种基于曲率无关方向扩散的非参数化主动轮廓噪声去除方法.通过曲率无关方向扩散避免了去噪过程对主动轮廓形状的影响,并通过非规则边缘控制函数进一步控制噪声去除过程对信号边缘与噪声的不同作用,在有效去除噪声的同时保证了高曲率轮廓的收敛性;在此基础上,提出一种针对低信噪比图像分割的改进型Chan-Vese主动轮廓模型.最后通过详细的实验证明了该方法和改进Chan-Vese主动轮廓模型的有效性.     

基于全局极小解Chan-Vese模型的SAR图像分割

活动轮廓模型是近年来最成功的分割模型之一。但由于SAR图像存在较强的斑点噪声,使用传统的Chan-Vese模型水平集分割方法会产生很多误分割。因此,需要对传统Chan-Vese模型进行改进,将非凸的Chan-Vese模型转换为凸优化问题,得到Chan-Vese模型的全局极小解。对凸优化Chan-Vese模型引入边缘检测算子,得到基于边缘和区域信息的全局极小解Chan-Vese模型。在水平集演化迭代过程中,引入一个新的迭代终止条件,可以敏感地判断演化曲线的变化幅度,根据设定条件,自动的停止迭代计算。针对合成图像和真实SAR图像进行分割实验,实验结果表明,提出的改进Chan-Vese模型能够快速、准确地提出图像中感兴趣目标,并具有较强的抗噪性。     

带线性正则化项的RSF模型

RSF（Region-Scalable Fitting）模型能够分割灰度不均一图像,但对活动轮廓的初始化和噪声较为敏感。运用理论探究与实验相结合的方法,基于RSF模型,添加一个新的水平集线性正则化项,得到了一个新的活动轮廓模型。实验表明,该模型能够分割灰度不均一图像,对初始轮廓的大小和位置不敏感,抗噪性也较强。     

基于Chan-Vese模型的SAR图像分割

由于SAR图像存在较强的斑点噪声,使用Chan-Vese模型水平集分割方法会产生很多误分割。同时,水平集解法存在计算量大、分割速度慢的问题。在Chan-Vese模型基础上,增加新的内能项——距离正则项,得到了一种改进的曲线演化模型。避免了水平集函数的周期性更新,具有更大的迭代步长,从而加快分割速度,并且提高Chan-Vese模型的抗噪性。对该模型采用人工合成图像和真实SAR图像进行分割实验,通过比较,可看出改进模型具有较高的数值精度和较快的分割速度。对于噪声很强的图像,使用增强Lee滤波进行预处理,可以进一步提高改进模型的分割速度和效果。实验结果表明:改进Chan-Vese模型能高效快速地完成SAR图像分割,具有较高的抗噪性。     

基于核函数的活动轮廓模型

为了改善活动轮廓模型的分割精度和效率,提出一种基于核函数的活动轮廓模型.该模型采用鲁棒的非欧氏距离度量构造能量泛函,提高了模型的分割精度;使用指数类型的核特征函数来提升收敛速度;最后在模型中还加入了水平集正则项,以避免水平集的重新初始化.实验结果表明,文中模型在分割精度和分割效率上都要强于Chan-Vese模型.     

结合全局和局部信息的活动轮廓模型

利用全局信息的C-V模型对轮廓初始化和噪声不敏感,但不能分割灰度不均的图像;利用局部信息的RSF模型能分割灰度不均的图像,但对轮廓初始化和噪声很敏感。针对该问题,基于C-V模型和RSF模型,提出一个新的水平集正则化项,给出一个用偏微分方程表示的结合全局和局部信息的活动轮廓模型。实验结果表明,该模型能分割灰度不均的图像,且允许灵活的轮廓初始化,抗噪性较强。     

基于变动权值的混合水平集图像分割模型

基于局部区域的活动轮廓模型(LRAC)分割图像时对初始轮廓的严重依赖性,提出一种基于局部和全局区域结合的水平集图像分割算法。结合Chan-Vese水平集模型和LRAC模型的特点,在构造水平集函数时定义了变动的权值参数,将水平集函数的局部和全局能量泛函项结合起来,其中,权重参数由图像梯度和图像演化曲线内外局部均值定义。另外,在水平集函数演化时采用窄带法,以减小计算的时间复杂度。实验结果表明,该算法模型兼有CV模型和LRAC模型的优点,比LRAC模型对初始轮廓选取的依赖性低,收敛速度快;比窄带CV模型的对目标边缘分割效果好。     

基于聚类信息的活动轮廓图像分割模型

基于传统Chan-Vese( CV)模型,结合图像聚类信息,提出一种有效的活动轮廓模型图像分割方法。该方法首先改进CV模型的能量泛函,考虑图像的梯度信息,提高图像分割的精确度。其次在能量泛函中添加图像的聚类信息系数K,并使用图像的聚类信息实现对水平集轮廓曲线的自动初始化。在分割处理彩色图像时,为提高分割效率,对彩色RGB图像的三通道进行加权处理。最后为能量泛函添加正则项,避免水平集的重新初始化,完成对灰度图像及彩色图像的快速精确分割。实验表明该方法的有效性。     

带H1正则项的C-V模型

C-V模型(CHAN T F, VESE L A. Active contours without edges. IEEE Transactions on Image Processing, 2001, 10(2): 266-277)是一个著名的基于区域的图像分割模型。它对活动轮廓的初始化和噪声不敏感,但分割的图像的范围不够广泛。因此,运用理论分析与实验相结合的方法,在C-V模型中添加H1正则项,对其进行了改进,提出了一个新颖的图像分割的能量泛函,并推导出了以偏微分方程形式表示的基于区域的自适应插值拟合的活动轮廓模型。实验表明：该模型能够分割某些原来C-V模型不适用的图像,它对初始轮廓的大小、位置的敏感性较小,抗噪性较强。     

An efficient local Chan-Vese model for image segmentation

In this paper, a new local Chan-Vese (LCV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, local statistical function and level set method. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a new penalizing energy. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented which is based on the length change of evolving curve. Particularly, we proposed constructing an extended structure tensor (EST) by adding the intensity information into the classical structure tensor for texture image segmentation. It can be found that by combining the EST with our LCV model, the texture image can be efficiently segmented no matter whether it presents intensity inhomogeneity or not. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with the well-known Chan-Vese (CV) model and recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.     

An improved region-based model with local statistical features for image segmentation

In this paper, we propose a new region-based active contour model (ACM) for image segmentation. In particular, this model utilizes an improved region fitting term to partition the regions of interests in images depending on the local statistics regarding the intensity and the magnitude of gradient in the neighborhood of a contour. By this improved region fitting term, images with noise, intensity non-uniformity, and low-contrast boundaries can be well segmented. Integrated with the duality theory and the anisotropic diffusion process based on structure tensor, a new regularization term is defined through the duality formulation to penalize the length of active contour. By this new regularization term, the structural information of images is utilized to improve the ability of capturing the geometric features such as corners and cusps. From a numerical point of view, we minimize the energy function of our model by an efficient dual algorithm, which avoids the instability and the non-differentiability of traditional numerical solutions, e.g. the gradient descent method. Experiments on medical and natural images demonstrate the advantages of the proposed model over other segmentation models in terms of both efficiency and accuracy.     

Active contour model for simultaneous MR image segmentation and denoising

In this paper, a new region-based active contour model is proposed for magnetic resonance image segmentation and denoising based on the global minimization framework and level set evolution. A new region fitting energy based on Nadaraya–Watson estimator and local image information is defined to enforce the curve evolution. By this improved region fitting term, the images with noise and intensity un-uniformity can be segmented and denoised. Inspired by the Perona–Malik diffusion equation, an edge-preserving regularization term is defined through the duality formulation to penalize the length of region boundaries. By this new regularization term, the edge information is utilized to improve the contour?s ability of capturing the edge and remaining smooth during the evolution. The energy functional of the proposed model is minimized by an efficient dual algorithm avoiding the inefficiency of the gradient descent method. Experiments on medical images demonstrate the proposed model provides a hybrid way to perform image segmentation and image denoising simultaneously.     

An integrated two-stage approach for image segmentation via active contours

A novel integrated two-stage approach is proposed for image segmentation, where the edge, global and local region information of images are in turn incorporated to define the intensity fitting energy. In the first stage, the Chan-Vese model flexibly assimilates the edge indicator function in the beginning, and then the Laplace operator is introduced to regularize the level set function when minimizing the energy functional. As an edge-based and global region-based active contour, it can be inclined to rapidly produce a coarse segmentation result. In the second stage, we further segment the image by absorbing the local region fitting energy, where its initialization is acquired by the final active contour of the first stage. In addition, we present a generalized level set regularization term, which efficiently eliminates the periodically re-initialization procedure of traditional level set methods and maintains the corresponding signed distance property. Compared with the first stage, the local object details are accurately segmented in the second stage, which can acquire an accurate segmentation result. Qualitative and quantitative experimental results demonstrate the accuracy, robustness and efficiency of our approach with applications to some synthetical and real-world images.

     

一种扩散张量脑脐服体图像分割算法

提出了一种基于矢量活动轮廓模型的扩散张量脑拼刀氏体图像分割算法,其利用矢量Chan-Vese模型构造了控制轮廓线演化方向的矢量符号压力函数,并将向量范数形式用于表达脑拼肌体组织的扩散张量各向异性,给出了具有全局与局部分割特性的矢量活动轮廓模型。10组真实大脑扩散张量图像分割结果表明,该算法对脑拼抵体结构的分割精确、稳定。