局部区域拟合的快速水平集图像分割方法

目的 由于灰度不均匀图像在不同目标区域的灰度分布存在严重的重叠,对其进行分割仍然是一个难题;同时,图像中的噪声严重降低了图像分割的准确性。因此,传统水平集方法无法鲁棒、精确、快速地对具有灰度不均匀性和噪声的图像进行分割。针对这一问题,提出一种基于局部区域信息的快速水平集图像分割方法。方法 灰度不均匀图像通常被描述为一个分段常数图像乘以一个缓慢变化的偏移场。首先,通过一个经过微调的多尺度均值滤波器来估计图像的偏移场,并对图像进行预处理以减轻图像的不均匀性;然后,利用基于偏移场校正的方法和基于局部区域信息拟合的方法分别构建能量项,并利用演化曲线轮廓内外图像灰度分布的重叠程度,构建权重函数自适应调整两个能量项之间的权重;最后,引入全方差规则项对水平集进行约束,增强了数值计算的稳定性和对噪声的鲁棒性,并通过加性算子分裂策略实现水平集快速演化。结果 在具有不同灰度不均匀性和噪声图像上的分割结果表明,所提方法不但对初始轮廓的位置、灰度不均匀性和各种噪声具有较强的鲁棒性,而且具有高达94.5%的分割精度和较高的分割效率,与传统水平集方法相比分割精度至少提高了20.6%,分割效率是LIC（local intensity clustering）模型的9倍;结论 本文提出一种基于局部区域信息的快速水平集图像分割方法。实验结果表明,与传统水平集方法相比具有较高的分割精度和分割效率,可以很好地应用于具有灰度不均匀和噪声的医学、红外和自然图像等的分割。     

基于局部区域信息的医学图像分割及偏移场矫正方法

针对医学图像中由于偏移场的存在而导致图像灰度不均匀的问题,提出了一种基于局部区域信息的医学图像分割及偏移场矫正方法,以矫正偏移场使图像变为灰度均匀。该方法利用图像局部区域信息,通过拟合图像和原始图像构造能量函数,采用变分水平集方法进行求解。实验结果表明,该方法能够有效地实现医学图像分割及偏移场矫正,与其它分割及偏移场矫正方法相比,该方法具有较高的分割及偏移场矫正的精度和效率。     

Multi-agent stochastic level set method in image segmentation

A stochastic structure for single and multi-agent level set method is investigated in this article in an attempt to overcome local optima problems in image segmentation. Like other global optimization methods that take advantage of random operators and multi-individual search algorithms, the best agent in this proposed algorithm plays the role of leader in order to enable the algorithm to find the global solution. To accomplish this, the procedure employs a set of stochastic partial differential equations (SPDE), each one of which evolves based on its own stochastic dynamics. The agents are then compelled to simultaneously converge to the best available topology. Moreover, the stochastic dynamics of each agent extends the stochastic level set approach by using a multi source structure. Each source is a delta function centered on a point of evolving front. Lastly, while the computational costs of these methods are higher than the region-based level set method, the probability of finding the global solution is significantly increased.     

结合全局与局部信息活动轮廓的非同质图像分割

目的 通过对现有基于区域的活动轮廓模型能量泛函的Euler-Lagrange方程进行变形,建立其与K-means方法的等价关系,提出一种新的基于K-means活动轮廓模型,该模型能有效分割灰度非同质图像。方法 结合图像全局和局部信息,根据交互熵的特性,提出新的局部自适应权重,它根据像素点所在邻域的局部统计信息自适应地确定各个像素点的分割阈值,排除灰度非同质分割目标的影响。结果 采用Jaccard相似系数-JS(Jaccard similarity)和Dice相似系数-DSC(Dice similarity coefficient)两个指标对自然以及合成图像的分割结果进行定量分析,与传统及最新经典的活动轮廓模型相比,新模型JS和DSC的值最接近1,且迭代次数不多于50次。提出的模型具有较高的计算效率和准确率。结论 通过大量实验发现,新模型结合图像全局和局部信息,利用交互熵特性得到自适应权重,对初始曲线位置具有稳定性,且对灰度非同质图像具有较好地分割效果。本文算法主要适用于分割含有噪声及灰度非同质的医学图像,而且分割结果对初始轮廓具有鲁棒性。     

A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement

This paper presents a novel level set method for complex image segmentation, where the local statistical analysis and global similarity measurement are both incorporated into the construction of energy functional. The intensity statistical analysis is performed on local circular regions centered in each pixel so that the local energy term is constructed in a piecewise constant way. Meanwhile, the Bhattacharyya coefficient is utilized to measure the similarity between probability distribution functions for intensities inside and outside the evolving contour. The global energy term can be formulated by minimizing the Bhattacharyya coefficient. To avoid the time-consuming re-initialization step, the penalty energy term associated with a new double-well potential is constructed to maintain the signed distance property of level set function. The experiments and comparisons with four popular models on synthetic and real images have demonstrated that our method is efficient and robust for segmenting noisy images, images with intensity inhomogeneity, texture images and multiphase images.     

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.     

结合全局信息的局部图像灰度拟合模型

针对局部图像拟合（LIF）模型对初始轮廓大小、形状和位置敏感的问题,提出一个结合全局信息的局部图像灰度拟合模型。首先,构造了一个基于全局图像信息的全局项;其次,将该全局项与LIF模型中的局部项线性组合;最后,得到了一个以偏微分方程形式存在的图像分割模型。数值实现采用有限差分法,同时采用高斯滤波器正则化水平集函数以确保水平集函数的光滑作用。在分割实验中,当选取不同的初始轮廓时,该模型均能得到正确的分割结果,且分割时间仅为LIF模型的20%到50%。实验结果表明,所提模型既对演化曲线初始轮廓的大小、形状和位置都不敏感,又能够有效地分割灰度不均图像,且分割速度较快。此外,在无初始轮廓的情形下,该模型能快速分割一些真实图像和人造图像。     

快速稳定的局部二元拟合分割算法

基于局部区域信息的局部二元拟合(LBF)模型在处理弱边界或灰度不均匀的图像分割方面有一定优势,但该方法非常依赖于初始轮廓,不当的初始轮廓不仅会导致分割时间较长,甚至分割失败。针对这一不足,提出一种快速稳定的LBF模型。首先通过添加带有变权系数面积项的LBF模型进行初始分类以获取较好的初始轮廓,然后采用传统的LBF模型对图像进行进一步的分割。实验证明,在保证良好分割效果的前提下,该方法对初始轮廓的选择更加灵活,分割速度明显快于传统的LBF模型。     

基于局部区域拟合模型的磁共振图像分割与偏移估计算法

磁共振(MR)图像的灰度通常是不均匀的,这种不均匀性是由于成像设备的缺陷导致产生了一种光滑的偏移场.一般的基于灰度统计特性的分割算法都是假设目标区域和背景区域图像的灰度分别是一致的,因此该类算法不能很好地应用于磁共振图像的分割.提出一种基于局部拟合模型的磁共振图像分割与偏移估计算法:利用图像的局部区域的灰度特性建立恢复...     

基于局部区域信息的水平集医学图像分割方法

针对医学图像中存在的亮度分布不均匀（intensity inhomogeneity）的特点,对Chan-Vese提出的基于Mumford-Shah模型的水平集分割图像的算法进行了改进。局部区域信息是对亮度分布不均匀图像进行准确分割的关键,但是传统的基于区域信息的C-V模型没有利用到这种局部区域的图像信息,因此无法正确分割强度分布不均匀图像。利用局部区域信息构造能量函数,提出了一种基于局部区域信息的改进C-V模型。该模型无需大量计算,水平集函数可快速收敛。MR图像、血管造影图像和X线骨折图像的实验结果证明了该方法的高效性。     

显著性驱动的局部相似拟合模型分割算法研究

灰度不均匀和噪声图像的分割是计算机视觉中的难点。现有的活动轮廓模型尽管能够取得较好的分割效果,但仍然对噪声图像分割效果不理想,初始轮廓曲线的选取敏感,优化易陷入局部极小导致演化速度慢等问题。针对该问题,首先使用局部区域灰度的均值和方差拟合高斯分布,构建新的能量泛函,均值和方差随着能量的最小化过程而变化,从而增强了灰度不均匀和噪声图像的分割能力。此外,结合视觉显著性检测算法获取待分割目标的先验形状信息,并自适应地创建水平集函数,从而降低了初始轮廓位置敏感性及计算时间复杂度,实现全自动的图像分割。实验结果证明,提出的算法可以用于灰度不均匀和噪声图像分割,并取得了较好的分割性能,消除了算法对初始轮廓位置敏感性,减少了迭代次数。     

自适应尺度的局部强度聚类图像分割模型

针对传统活动轮廓模型无法精确分割强度不均匀图像，并且对尺度参数比较敏感的问题，提出了一种基于区域信息的自适应尺度的活动轮廓模型。根据图像的局部熵构建自适应尺度算子，利用图像的局部强度聚类性质构建能量函数。使用一组平滑基函数的线性组合来表示偏移场，这样可以增加模型的稳定性。通过最小化该能量，所提模型能够同时分割图像和估计偏移场，并且估计的偏移场可以用于强度不均匀校正。实验结果表明，与其它4种模型相比，该模型拥有更高的分割精确度，且分割结果对水平集函数的初始化和噪声具有鲁棒性。     

Robust level set image segmentation via a local correntropy-based K-means clustering

It is still a challenging task to segment real-world images, since they are often distorted by unknown noise and intensity inhomogeneity. To address these problems, we propose a novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering. Due to the correntropy criterion, the clustering algorithm can decrease the weights of the samples that are away from their clusters. As a result, LCK based clustering algorithm can be robust to the outliers. The proposed LCK clustering algorithm is incorporated into the region-based level set segmentation framework. The iteratively re-weighted algorithm is used to solve the LCK based level set segmentation method. Extensive experiments on synthetic and real images are provided to evaluate our method, showing significant improvements on both noise sensitivity and segmentation accuracy, as compared with the state-of-the-art approaches.     

A new level set method for inhomogeneous image segmentation

Intensity inhomogeneity often appears in medical images, such as X-ray tomography and magnetic resonance (MR) images, due to technical limitations or artifacts introduced by the object being imaged. It is difficult to segment such images by traditional level set based segmentation models. In this paper, we propose a new level set method integrating local and global intensity information adaptively to segment inhomogeneous images. The local image information is associated with the intensity difference between the average of local intensity distribution and the original image, which can significantly increase the contrast between foreground and background. Thus, the images with intensity inhomogeneity can be efficiently segmented. What is more, to avoid the re-initialization of the level set function and shorten the computational time, a simple and fast level set evolution formulation is used in the numerical implementation. Experimental results on synthetic images as well as real medical images are shown in the paper to demonstrate the efficiency and robustness of the proposed method.     

Semi-supervised statistical region refinement for color image segmentation

Some authors have recently devised adaptations of spectral grouping algorithms to integrate prior knowledge, as constrained eigenvalues problems. In this paper, we improve and adapt a recent statistical region merging approach to this task, as a non-parametric mixture model estimation problem. The approach appears to be attractive both for its theoretical benefits and its experimental results, as slight bias brings dramatic improvements over unbiased approaches on challenging digital pictures.     

一种融入局部信息的直觉模糊C-均值聚类图像分割算法

模糊C-均值(FCM)算法对图像噪声敏感,聚类过程中只考虑图像的数值特征信息而忽略像素间空间约束关系,同时单一隶属度并不能充分描述图像的不确定性,这使得基于FCM的图像分割不够准确.融入局部信息的改进FCM算法虽然对图像噪声有一定鲁棒性,但对图像细节保持不够,难以分割微小区域.针对上述问题,提出一种基于直觉模糊集的改进模糊C-均值(IFS_FCM)图像分割算法.该方法将直觉模糊集理论融入到FCM中,充分考虑图像的不确定性,同时在目标函数中引入空间邻域信息,使得该分割算法对噪声鲁棒性增强的同时还能保持图像细节信息.实验结果表明,IFS_FCM能获得更加理想的图像分割效果.     

结合全局和局部信息的水平集图像分割方法*

LBF模型对初始轮廓大小和位置非常敏感,并且只考虑了图像的局部信息,没有考虑图像的全局信息。CV模型利用图像全局信息,对初始轮廓具有较强的鲁棒性。两种模型对椒盐噪声污染的图像不能取得令人满意的结果。针对以上问题, 在原有CV模型和LBF模型能量函数基础上,各自构造一个新的能量拟合项,增强对高斯噪声和椒盐噪声的抗噪性。采用新构造的CV模型,使用图像的全局信息得到粗分割轮廓。以粗分割轮廓作为新构造LBF模型的零水平集,利用图像的局部信息得到图像的精确分割结果。同时提出一种新的边缘检测算子,重新定义边缘停止函数,进一步提高模型的抗噪性。相较于CV模型,LBF模型,结合全局和局部信息的Wang模型和Qi模型,提出模型能得到更优的图像分割结果,具有较强的抗噪性。     

木材显微图像的局部水平集分割方法

获取木材显微图像中的细胞组织对于分析木材的种类、材性,以及天气变化等均有重要的意义,而这依赖于图像分割技术。针对木材组织的不均匀性,以及标本制作和获取过程中带来的噪声,将水平集方法中边缘型和区域型两种模型引入,同时结合局部图像信息来提高局部不均匀图像的分割性能。在图像初始分割基础上,通过面积阈值去除水泡等杂质,最终提取导管组织。实验结果表明,提出的模型所得到的分割图像较平滑,而且噪声明显减少,可有效分割局部不均匀木材显微图像。     

基于声纳图像的水平集分割算法研究

针对现有的图像分割方法无法准确地分割声纳图像的问题,提出了一种改进的水平集声纳图像分割方法。介绍了LBF能量模型,借鉴其无重初始化的水平集演化思想。为克服声纳图像中复杂背景带来的负面效应,利用形态学顶帽—底帽变换对声纳图像进行预处理,并在此基础上进行无需初始化的水平集分割。进行仿真对比实验,实验结果显示:与LBF能量模型相比,改进的水平集分割方法更加适应于背景不均匀的声纳图像分割。     

快速的局部二值拟合优化分割算法

针对灰度不均匀图像难以正确分割和分割结果依赖于初始轮廓的问题,提出一种快速稳定的分割算法,首先通过自适应距离保持水平集演化(ADPLS)算法进行初始分割以获取较好的初始轮廓,然后采用局部二值拟合(LBF)模型进行快速分割。实验表明,改进后的模型有良好的分割效果,较好地解决了分割速度、精度及稳定性之间的矛盾。