变分水平集方法在灰度不均图像分割中的应用

灰度不均严重影响图像分割的准确性,主动轮廓模型广泛应用于图像分割中。为了克服灰度不均对图像分割的影响,提出了一种基于变分水平集的主动轮廓模型。该模型利用了图像局部统计信息的均值和方差,适合对灰度不均图像分割。为了检验算法的性能,利用该算法和经典算法作对比实验,结果表明,不管是对合成图像还是真实图像的分割,都验证了该方法的有效性,而且该方法在曲线演化过程中无需重新初始化水平集函数,在一定程度上减少了计算量。     

融合局部和全局图像信息的活动轮廓模型

为了克服局部图像拟合模型对轮廓初始化敏感的不足,结合改进C-V模型,提出一种融合局部和全局图像信息的活动轮廓模型.首先由改进C-V模型的全局灰度拟合力和局部图像拟合模型的局部灰度拟合力的一个线性组合来构造水平集演化力,然后通过调整这2个拟合力的权重以提升该模型对轮廓初始化的灵活性,最后利用高斯滤波正则水平集函数法实现水平集函数的正则化.实验结果表明,对于一些真实和人造图像,文中模型显示了对轮廓初始化的鲁棒性,以及较好地处理灰度不均图像的能力.     

The integration of directional information and local region information for accurate image segmentation

The original local binary fitting (LBF) model is sensitive to contour initialization and thus easily obtains an inaccurate result due to improper initialization. This paper presents a new method that not only can arrive at sub-pixel accuracy, but also allows for more flexible initialization of the contour. Two important terms play main role in our new method. One is an image gradient alignment term (IGA) which uses the directional information of the image gradient, the other is a local intensity fitting term (LIF) which makes use of local region information. The integration of the above two terms prevents our method from being sensitive to contour initialization. In addition, a global intensity fitting term (GIF) multiplied by a stopping function is included, which can speed up our algorithm while do not influence the accuracy of the segmentation result. Using the simple central difference, the gradient descend flow equation for the level set function can be easily and efficiently implemented. The results on several synthetic and real images demonstrate the effectiveness and accuracy of our method.     

结合统计和梯度信息的高效活动轮廓模型

提出一种由统计和梯度信息驱动的活动轮廓模型。该模型有效利用梯度信息使演化轮廓线快速精确地定位到物体的边缘;同时,由局部统计信息和全局统计信息构造符号压力函数,减少噪声对轮廓线演化的影响。另外,模型利用局部统计信息能够有效处理灰度分布不均的图像,全局信息的利用避免了演化轮廓线陷入局部最小,因此,该模型可以任意设置初始轮廓线。最后通过高斯卷积核对水平集函数规则化,避免了传统模型中计算代价高昂的重新初始化和规则化。实验结果表明,提出的模型不仅能够在任意初始轮廓下精确有效地分割灰度分布均匀的图像和不均匀的图像,而且对噪声具有较好的鲁棒性。     

基于双符号压力函数的活动轮廓图像分割方法

为了解决测地线模型和CV模型无法同时对弱边界、灰度不均匀图像进行分割的问题,提出一种基于双符号压力函数的活动轮廓图像分割方法。首先,基于图像统计信息定义分割灰度不均匀图像的符号压力函数,基于内部和外部灰度均值给出轮廓曲线内外的全局区域灰度均值的加权组合函数,运用图像全局信息定义分割弱边界图像的符号压力函数;然后,结合统计信息的符号压力函数和全局信息的符号压力函数（简称“双符号压力函数”）,通过增加组合的权值系数,设计新的水平集演化方程;最后,将双符号压力函数引入到二值选择和高斯滤波正则化水平集模型中,构建一种基于双符号压力函数的活动轮廓图像分割算法。仿真实验结果表明,该算法能够有效地分割弱边界、灰度不均匀的图像,同时对噪声也有一定的抗干扰性。     

基于局部C-V水平集的CT肝脏病灶提取

本文针对肝脏CT图像的特点,提出一种局部C-V模型水平集算法对肝脏病灶进行分割。该算法首先在肝脏内部选择包含病灶的局部图像,再采用局部最大类间方差法进行预分割,根据最佳阈值确定初始水平集,最后采用局部C-V模型对初始轮廓曲线进行演化。实验结果表明该方法能较好地提取出肝脏病灶。     

Characterization and compensation of OLED aging in a digital AMOLED system

Organic light‐emitting diode (OLED) aging is the root cause for image sticking artifact and considered as the toughest problem besides the low yield problem of active‐matrix organic light‐emitting diode (AMOLED) displays. Digital driving can eliminate Mura artifact and allow a similar yield like LCD. However, it is more prone to OLED aging than analog driving, so that the lifetime will become shorter. In this paper, we pursue the approach to measure the pixel current and compensate OLED I–V drift. Information gained from electrical measurements during the lifetime of the display may be correlated to electro‐optical drift, particularly the current efficiency. The aging model has to consider the dependence of I–V drift and efficiency loss on the operation point/voltage for each subframe. Specific compensation algorithms have been developed. Two AMOLED prototypes (1.5 and 2.8 in.) were validated. Burned‐in pattern can be compensated, so the concept has been proven as effective. With the method described in this paper, digital AMOLED may reach a similar and even significantly higher lifetime than an analog AMOLED.     

Active contour model combining region and edge information

A novel active contour model is proposed by combining region and edge information. Its level set formulation consists of the edge-related term, the region-based term and the regularization term. The edge-related term is derived from the image gradient, and facilitates the contours evolving into object boundaries. The region-based term is constructed using both local and global statistical information, and related to the direction and velocity of the contour propagation. The last term ensures stable evolution of the contours. Finally, a Gaussian convolution is used to regularize the level set function. In addition, a new quantitative metric named modified root mean squared error is defined, which can be used to evaluate the final contour more accurately. Experimental results show that the proposed method is efficient and robust, and can segment homogenous images and inhomogenous images with the initial contour being set freely.     

结合分布度量统计建模的主动轮廓图像分割

图像分割是数字图像处理中不可或缺的关键步骤。为了解决传统主动轮廓模型针对非匀质图像分割结果不准确且分割效率低的问题,提出一种结合分布度量统计建模的主动轮廓图像分割算法。所提算法的能量驱动力兼顾了图像的全局统计建模信息和其他混合灰度分布信息,使得分割曲线能够更加精确地演化至目标边缘。分布度量能量驱动力定义为轮廓内外概率密度函数定义的比率距离的方差,该能量驱动力基于图像全局信息统计建模,能够更加精确地描述轮廓曲线内外的能量变化;混合灰度分布能量驱动力由图像灰度值与融合均值与中值的区域拟合中心的L2范数表示。将分布度量能量驱动力与混合灰度分布能量驱动力组合形成新的能量泛函,利用水平集方法和梯度下降法迭代求得该能量泛函的最小值,以获得最终的图像分割结果。与传统CV（Chan Vese）模型、LBF（Local Binary Fitting）模型等四种算法的图像分割结果相比,所提模型在主观视觉效果、对初始轮廓的敏感性、运行时间和迭次次数方面均具有较大优势。     

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 new active contour remote sensing river image segmentation algorithm inspired from the cross entropy

The CV (Chan–Vese) model is a piecewise constant approximation of the Mumford and Shah model. It assumes that the original image can be segmented into two regions such that each region can be represented as constant grayscale value. In fact, the objective functional of the CV model actually finds a segmentation of the image such that the within-class variance is minimized. This is equivalent to the Otsu image thresholding algorithm which also aims to minimize the within-class variance. Similarly to the Otsu image thresholding algorithm, cross entropy is another widely used image thresholding algorithm and it finds a segmentation such that the cross entropy of the segmented image and the original image is minimized. Inspired from the cross entropy, a new active contour image segmentation algorithm is proposed. The region term in the new objective functional is the integral of the logarithm of the ratio between the grayscale of the original image and the mean value computed from the segmented image weighted by the grayscale of the original image. The new objective functional can be solved by the level set evolution method. A distance regularized term is added to the level set evolution equation so the level set need not be reinitialized periodically. A fast global minimization algorithm of the objective functional is also proposed which incorporates the edge term originated from the geodesic active contour model. Experimental results show that, the algorithm proposed can segment images more accurately than the CV model and the implementation speed of the fast global minimization algorithm is fast.     

结合分数阶微分和图像局部信息的CV模型

目的 由于CV模型仅利用了图像的全局信息,其对灰度不均匀图像的分割效果不理想,同时在分割弱边缘和弱纹理图像时,优化易陷入局部最优从而导致分割效率低下,且对初始位置的选择较为敏感。针对这些问题,提出一种结合分数阶微分和图像局部信息的CV模型。方法 首先将分数阶梯度信息融入图像的局部信息中,用来替代CV模型的整数阶全局信息,并建立自适应计算分数阶最佳阶次的数学模型,然后在模型中加入符号距离的约束项。结果 一方面,用局部信息代替全局信息,可以在一定程度上解决CV模型对灰度不均匀图像分割效果不理想的问题。另一方面,将Grünwald-Letnikov分数阶梯度信息融合到局部信息中,当分数阶阶次0 < α < 1时,增加了图像灰度不均匀、弱边缘、弱纹理区域的梯度信息,从而增加了演化驱动力避免演化曲线陷入局部最优,有效地解决了图像因灰度变化不大导致演化曲线驱动力小的问题,在一定程度上解决了模型对初始轮廓位置选择和对噪声敏感的问题。同时为了解决人工选取最佳分数阶阶次费时费力的问题,根据图像的梯度模值和信息熵建立计算分数阶最佳阶次的数学模型,将此自适应分数阶模型应用到算法之中,以自适应确定最佳分数阶阶次。此外,为了避免模型的重新初始化,在模型中加入符号距离的约束项,从而提高了曲线的演化效率。结论 理论分析和实验结果均表明,该算法能够较好地分割灰度不均匀、弱边缘和弱纹理区域的图像,并能根据图像特征自适应确定最佳分数阶阶次,提高了分割精度和分割效率,且对初始轮廓位置选择及噪声均具有一定的鲁棒性。     

一种改进的变分水平集的图像分割算法

为了解决灰度不均匀现象对医学图像的干扰问题,提出了基于局部极性信息的活 动轮廓模型。通过引入局部图像信息,该模型能有效地分割灰度不均匀图像。在规则化项中增 加的能量惩罚项,使得水平集函数在演化过程中保持为近似的符号距离函数。该算法将图像分 割问题归结为曲线能量泛函的最小化,首先建立包含局部灰度信息（极性信息）和改进的符号 距离函数的曲线演化能量泛函;然后采用变分水平集方法求解能量函数的最小值,得到最终的 分割结果。真实医学图像和人工合成图像的实验结果表明,此方法对灰度不均匀的医学图像有 较高的分割精确度,在图像分割速度上有较大提高。由于利用了局部灰度信息,可以有效地分 割灰度不均匀的医学图像,而改进后的变分水平集可以完全避免重新初始化,使得图像分割效 率大大提高了。     

结合局部熵能量泛函与非凸正则项的图像分割

为了克服灰度不均匀对图像分割的影响，结合CV模型的全局能量项和LBF模型的局部能量项，引入图像局部熵信息和非凸正则项，构造新的能量泛函，提出了结合局部熵的局部能量泛函与非凸正则项的图像分割算法。该算法首先采用CV模型中的全局能量泛函得到图像的大致演化轮廓；通过构建具有局部熵信息的局部能量泛函，实现对图像的精确分割。然后，利用非凸正则项作为图像演化过程中零水平集逼近目标的又一驱动力驱动曲线演化和边缘保护。该算法利用变分水平集方法将这一新构建的能量泛函进行最小化，通过迭代更新水平集函数，完成曲线演化。最后，对比实验表明，所提出的算法可以高效、准确地分割灰度不均匀图像。     

Robust global and local fuzzy energy based active contour for image segmentation

Though various image segmentation techniques have been developed, it is still a very challenging task to design a robust and efficient algorithm to segment (noisy, blurred or even discontinuous edged) images having high intensity inhomogeneity or non-homogeneity. In this article, a robust fuzzy energy based active contour, using both global and local information, is proposed to detect objects in a given image based on curve evolution. The local energy is generated by considering both local spatial and gray level/color information. The proposed model can better deal with images having high intensity inhomogeneity or non-homogeneity, noise and blurred boundary or discontinuous edges by incorporating local energy term in the proposed active contour energy function. The global energy term is used to avoid unsatisfactory results due to bad initialization. In this article, instead of solving the Euler–Lagrange equation, a level set based optimization is used for the convergence. We show a realization of the proposed method and demonstrate its performance (both qualitatively and quantitatively) with respect to state-of-the-art techniques on several images having such kind of artifacts. Analysis of results concludes that the proposed method can detect objects from given images in a better way than the existing ones.     

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。     

基于凸松弛方法的医学 B 超图像快速分割

利用活动轮廓线方法进行图像分割的一个重要缺陷是目标函数是非凸的, 这不仅使得分割结果容易陷于局部极小, 而且还使得一些快速算法无法开展.本文首先从贝叶斯风险估计的方法出发,针对B超幅度图像, 给出一种基于Rayleigh分布的活动轮廓线模型. 然后结合凸松弛的方法,得到一个新的放松的凸模型.原有模型和放松后模型的关系可由定理1给出. 最后结合分裂Bregman算法, 给出基于B超分割模型的快速算法.与传统梯度下降法相比较,本文提出的算法不仅能得到全局最优解,而且在算法收敛速度上也 大大优于梯度下降法.     

结合梯度和区域信息的多尺度水平集图像分割

提出了一种结合梯度和区域信息的多尺度水平集图像分割算法。该算法结合梯度和区域信息构造能量函数,在梯度约束项中,构建了一个基于小波高频分量的边缘检测函数,在区域约束项中,运用经典C-V模型的区域项,得到混合C-V模型,采用变分法求解,并消除了水平集的重初始化。利用小波变换首先在逼近图像中运用混合C-V模型得到粗分辨图像的一个粗尺度分割,再对当前粗尺度下的最终轮廓线作内插操作,将得到的近似轮廓曲线作为初始水平集函数在原图像中运用消除重初始化的C-V模型演化得到最终的分割。实验结果表明,在同样的模型参数条件下,该方法具有比传统方法更高的演化效率和分割质量。     

结合全局和局部信息的“两阶段”活动轮廓模型

目的 LBF(local binary fitting)模型用每个像素点的邻域信息来拟合局部能量,对灰度不均匀图像可以得到很好的分割效果。但是LBF模型只考虑了图像的局部信息,没有考虑全局信息,因此它对初始轮廓大小、形状及位置都非常敏感。针对以上问题,结合全局和局部信息,提出"两阶段"活动轮廓模型。方法第1阶段,采用退化的CV(Chan-Vese)模型,利用图像的全局信息(灰度均值)快速为图像的目标大致定位;第2阶段,以第1阶段结束时的水平集函数的零水平集为第2阶段的初始轮廓,利用图像的局部信息(局部高斯拟合)得到更加精确的分割结果。结果实验结果表明,该"两阶段"活动轮廓模型保留了LBF模型分割灰度不均匀图像的能力。结论改进后的模型较LBF模型对各种初始轮廓(大小、形状、位置)有较强的鲁棒性,以及较强的抗噪性。     

A novel active contour model for image segmentation using local and global region-based information

In this paper, we propose a novel level set geodesic model for image segmentation. In our model, we define a hybrid signed pressure force (SPF) function integrating local and global region-based information to segment inhomogeneous images. The local region-based SPF utilizes mean values on local circular regions centered in each pixel. By introducing the local image information, the images with intensity inhomogeneity can be effectively segmented. In order to reduce the dependency on complex initialization, we incorporate a global region-based SPF into this model to develop a hybrid SPF. The global SPF and the local SPF are adaptively balanced by an adaptive weight. In addition, we also extend this model to four-phase level set formulation for brain MR image segmentation. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need for computationally expensive re-initialization. Experimental results indicate that the proposed method achieves superior segmentation performance in terms of accuracy and robustness.