多区域SAR图像分割的改进水平集方法

由于合成孔径雷达（SAR）图像易受相干斑噪声的影响,光学图像的分割方法并不适用于SAR图像,更不能获得精确的分割结果对比,因此,首先基于GA^0统计模型定义能量映射函数以代替像素值进行后续处理,减小相干斑的影响;其次,使用水平集算法对处理后的图像进行分割处理,选用了一种形式更为简单的水平集函数,并可以较容易地推广到多区域SAR图像分割情况。实验结果表明,该方法可以减少相干斑噪声对SAR图像分割过程的不良影响,具有较好的准确性。     

A level set method for image segmentation based on Bregman divergence and multi-scale local binary fitting

Multimedia Tools and Applications - Image segmentation is an important processing in many applications such as image retrieval and computer vision. The level set method based on local information...     

基于多尺度特征融合的SAR图像分割

由于存在相干斑噪声的影响,给SAR图像分割造成很大的困难,提出一种基于多尺度特征融合的SAR图像分割方法。该方法利用快速离散curvelet变换提取图像的纹理特征,利用平稳小波变换提取图像的统计特征,将两种多尺度特征融合成高维的特征向量,采用模糊C均值聚类的方法进行分割。在仿真SAR图像和真实SAR图像的分割实验结果表明,提出的方法优于单独采用小波变换进行SAR图像分割的方法,在消除均质区内碎块的同时,使得边界更为精准和平滑。     

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.     

一种基于多尺度马尔可夫随机场的SAR图像分割算法

利用小波变换模大值边缘检测算法得到SAR图像不同尺度下的边缘信息,再利用MRF分割算法对SAR图像进行分割。实验结果表明,该方法改善了SAR图像分割的质量,有效地改善了MRF图像分割算法的方向灵敏性。     

A level set method based on the Bayesian risk for medical image segmentation

This paper proposes an alternative criterion derived from the Bayesian risk classification error for image segmentation. The proposed model introduces a region-based force determined through the difference of the posterior image densities for the different classes, a term based on the prior probability derived from Kullback-Leibler information number, and a regularity term adopted to avoid the generation of excessively irregular and small segmented regions. Compared with other level set methods, the proposed approach relies on the optimum decision of pixel classification and the estimates of prior probabilities; thus the approach has more reliability in theory and practice. Experiments show that the proposed approach is able to extract the complicated shapes of targets and robust for various types of medical images. Moreover, the algorithm can be easily extendable for multiphase segmentation.     

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.     

基于小波多尺度聚类水平集的医学图像分割

针对医学图像低对比度、灰度不均匀等特点,提出了一种小波多尺度聚类水平集的图像分割方法,能够很好地解决医学图像灰度不均匀的问题。首先,利用小波多尺度分析的良好信噪分离能力提取各尺度下图像的有效边缘信息,将边缘信息添加到水平集模型的能量函数中从而提高模型的局部控制能力。然后,基于灰度不均匀的图像模型,派生出对于感兴趣区域的局部灰度聚类,在每个点的邻域内定义基于灰度的局部聚类准则函数。将局部聚类准则函数转化为全局准则函数。在水平集框架中,该准则根据水平集函数定义了代表图像域划分的能量项和引起图像强度不均匀的偏置域。最后,从小波变换的顶层低频子带图像开始逐层采用改进的聚类水平集方法分割图像,并将分割结果通过插值方式传递至下一层作为分割的初始轮廓,最终实现灰度不均匀医学图像的分割。实验结果表明,该方法能够有效地分割医学图像,具有计算更加鲁棒稳定、效率更高和更加准确的优点。     

基于水平集方法的软组织图像序列分割

医学图像分割是医学图像处理中的关键问题之一.图像序列的分割操作是医学图像三维重建的必要准备,而软组织图像分割则是医学图像分割中的一大难点.基于曲线演化理论的,借助偏微分方程等数学工具的水平集方法已经被广泛应用于医学图像分割领域.介绍了水平集方法的数学模型,并设计了一种基于窄带水平集方法的,专门针对软组织图像分割的算法.用边界追踪等方法提取第一层图片中的软组织相关轮廓;将它们作为初始水平集曲线,再利用窄带水平集方法进行演化;经过两个阶段的迭代处理,最终自动分割出整个软组织图像序列.实验表明该算法具有较高效率、分割结果精确,所产生的分割结果可以作为三维重建的合适的数据集.     

基于水平集的医学图像快速分割方法

针对高噪声、低对比度的医学图像难以快速准确分割的问题,结合基于像素的传统方法和基于水平集的活动轮廓模型,提出了一种混合的医学图像分割新技术.首先依据待分割对象的先验知识交互选取感兴趣区域.然后由传统的方法和基于水平集的C-V模型结合实现感兴趣区域图像的预分割.预分割的结果直接作为窄带变分水平集模型的初始轮廓,演化曲线在很短的时间内准确收敛到待分割物体的边缘.     

基于水平集的手指静脉图像分割

针对手指静脉图像中存在的弱边缘、灰度不均匀以及低对比度等现象,提出一种结合偶对称Gabor滤波与水平集思想的分割算法,并应用于手指静脉图像的分割。首先,使用偶对称Gabor滤波算法,对手指静脉图像从8个不同的方向分别进行滤波运算;然后,根据8个方向上的滤波结果进行图像重建,得到目标与背景灰度对比度显著提高的图像;最后,应用结合局部与全局信息的水平集方法对手指静脉图像进行分割。将所提算法与Li等水平集算法(LI C, HUANG R, DING Z, et al. A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. MICCAI'08: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II. Berlin: Springer, 2008: 1083-1091)、Legendre水平集(L2S)算法相比,所提算法在分割精度评价标准面积差异(AD)百分比上分别降低了1.116%、0.370%,相对差异度(RDD)分别降低了1.661%、1.379%。实验结果表明,与传统只考虑局部信息或全局信息的水平集图像分割算法相比,所提算法能取得更高的分割精度。     

A level set method for oil slick segmentation in SAR images

Despite much effort and significant progress in recent years, image segmentation remains a challenging problem in image processing, especially for the low contrast, noisy synthetic aperture radar (SAR) images. This paper explores the segmentation of oil slicks using a partial differential equation (PDE)‐based level set method, which represents the slick surface as an implicit propagation interface. Starting from an initial estimation with priori information, the level set method creates a set of speed functions to detect the position of the propagation interface. Specifically, the image intensity gradient and the curvature flow are utilized together to determine the speed and direction of the propagation. This allows the front interface to propagate naturally with topological changes, significant protrusions and narrow regions, giving rise to stable and smooth boundaries that discriminate oil slicks from the surrounding water. As the speckles are removed concurrently while the front interface propagates, the pre‐filtering of noise is saved. The proposed method has been illustrated by experiments on oil slick segmentation using the ERS‐2 SAR images. Its advantages over the traditional image segmentation approaches have also been demonstrated.     

自适应模型的水平集图像分割方法

水平集广泛应用于图像分割。给出基于传统C-V和GAC模型的水平集方法,在此基础上,介绍了一种结合C-V模型和GAC模型并根据图像特征选择性融入图像局部信息的自适应模型的水平集分割方法。通过实例分析,证明了该方法对分割弱边缘和灰度渐进的图像是有效的,并且抗噪声性能较好。     

基于描述方法的SAR图像分割*

针对减少SAR图像分割中自由参数的问题,提出了基于最小描述长度的SAR图像分割方法。该方法经对数变换将SAR图像乘性噪声转换为加性噪声,对其建立描述模型,在描述长度最短意义上计算出重建图像,在假设SAR图像各区域实际地物后向散射特性对应的像素值恒定的前提下,该重建图像即为SAR图像的分割结果。该方法在分割的同时很好地抑制了SAR图像的相干斑噪声,保留了原始SAR图像的区域边界,并且不需要参数调节,整个分割过程自动完成,是一种非监督SAR图像分割方法。给出了该方法的具体实现步骤,实验结果验证了该方法的有效性。     

一种新的基于区域竞争模型的水平集医学图像分割方法

传统的基于梯度模型的水平集分割方法在水平集曲线演化过程中存在着边界泄漏问题。针对这个问题,提出了一种基于改进区域竞争模型的水平集分割方法。本方法首先通过概率分布公式计算出水平集曲线属于目标区域和背景区域的概率;其次,将概率差值连同权重因子添加到水平集函数方程中,使曲线在演化过程中能量函数达到最小;最终,利用图像的区域信息提高水平集曲线识别边界的能力。实验结果表明该方法能够很好地实现医学图像的分割。     

A level set method based on local direction gradient for image segmentation with intensity inhomogeneity

Multimedia Tools and Applications - Many medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods...     

一种新的水平集图像分割方法

对Chan-Vese提出的基于简化Mumford-Shah区域最优划分模型和测地线主动轮廓模型在水平集框架下的物理机理进行了分析,在充分考虑其模型优点的基础上,通过构造新的能够整合局部边缘信息和全局区域信息的演化函数对上述模型所存在问题进行了针对性处理,得到了一种新的水平集图像分割模型。人工合成图像和红外光学图像的仿真结果表明,在同样的模型参数条件下,该文模型具有比传统CV模型和GAC模型更高的演化效率和分割质量。     

基于Chan-Vese水平集的梯度加速分割模型

为了提高图像分割的速度和精度,提出了一种新的基于Chan-Vese水平集模型(C-V模型)的梯度加速分割模型.首先,在C-V模型的能量函数中加入一个内部能量项,抵消演化过程中水平集函数和符号距离函数的偏差,从而消除分割中周期性重新初始化的过程;其次,提出了梯度加速项,通过感兴趣区域的图像特征,快速得到该区域的边界,且能够提高弱边界的分割精度.实验证明,提出的方法不仅能够加速特定区域的分割、提高分割精度,还能保持分割过程的稳定性.     

A novel retrieval method for remote sensing image based on statistical model

With the increasing number of high-resolution remote sensing (HRRS) image technologies, there is an interest in seeking a way to retrieve images efficiently. In order to describe the images with abundant texture information more concisely and accurately, we propose a novel remote sensing image retrieval approach based on the statistical features of non-subsampled shearlet transform (NSST) coefficients, according to which we set up a model using Bessel K form (BKF). First, the remote sensing (RS) image is decomposed into several subbands of frequency and orientation using the non-subsampled shearlet transform. Then, we use the Bessel K distribution model is utilized to describe the coefficients of NSST high-frequency subband. Next, the BKF parameters are selected to serve as the texture feature to represent the characteristics of image, namely BKF statistical model feature (BSMF), and the feature vector of each image is created by combination with parameters at each high-pass subband. Both the experiment and theory indicate that the BKF distribution is highly matched with the statistical features of NSST coefficients within high-pass subbands. In our experiments, we applied the proposed method to two general RS image datasets- The UC Merced land use dataset and the Sydney dataset. The results show that our proposed method can achieve a more robust and commendable performance than the state-of-the-art approaches.     

基于层次HMM的运动目标分割

提出对差分图像用三层统计模型表示的思想：前景运动汽车层、背景运动汽车层和运动阴影层,并分别建立了各层的统计模型,应用HMM对运动图像序列进行模型参数估计,通过模型进行运动汽车分割。HMM利用图像序列帧之间的图像像素空间相关性和时间相关性,从而完成模型参数的识别。通过MAP算法完成模型参数具体化,不但用模型完成图像前景目标的分割,同时在分割中自然区别了背景运动目标和阴影,实现了复杂背景图像的运动汽车分割。实验结果表明方法能够有效地完成分割目的。