Image Compression Using KLT, Wavelets and an Adaptive Mixture of Principal Components Model

In this paper, we present preliminary results comparing the nature of the errors introduced by the mixture of principal components (MPC) model with a wavelet transform and the Karhunen Loève transform (KLT) for the lossy compression of brain magnetic resonance (MR) images. MPC, wavelets and KLT were applied to image blocks in a block transform coding scheme. The MPC model partitions the space of image blocks into a set of disjoint classes and computes a separate KLT for each class. In our experiments, though both the wavelet transform and KLT obtained a higher peak signal to noise ratio (PSNR) than MPC, according to radiologists, MPC preserved the texture and boundaries of gray and white matter better than the wavelet transform or KLT.     

一种结合位置信息的混合模型图像分割方法

提出了一种新的受位置约束的混合模型图像分割方法。该方法在独立混合模型的基础上,采用空间滤波方法对像素所属分量的后验概率进行修正,在混合模型中隐含地加入了像素的空间位置信息。这种结合位置信息的方法为混合模型的分量选择提供了一种有效的实现方式。与其它的受位置限制的混合模型相比,该方法没有引入额外的模型参数,并且无需采用模型选择准则,可以实现自动的混合分量个数的选择。     

基于混合高斯模型的三马尔可夫场红外图像分割

针对马尔可夫随机场在红外图像分割方面存在的问题,给出了一种基于混合高斯模型的三马尔可夫场红外图像分割算法.三马尔可夫场在马尔可夫随机场的基础上通过引入一个附加随机场和全体随机变量服从马尔可夫性假设,克服了马尔可夫场算法中对条件概率分布相互独立的要求,并赋予该附加随机场对目标和背景区域的标识作用,其中采用混合高斯模型作为三马尔可夫随机场的先验模型.仿真结果表明,文中提出的基于混合高斯模型的三马尔可夫场红外图像分割算法能够实现复杂背景的红外图像准确分割,得到较为理想的分割效果.     

基于图像分割的置信传播立体匹配算法研究

在采用全局优化的立体匹配时,利用传统的基于像素点的置信传播算法进行消息传输存在两个不足:计算量大和单个像素点容易导致误差.利用图像分割思想来弥补这两个不足,提出基于图像分割的置信传播算法,利用分割区域代替像素点,视差平面模板集代替视差搜索空间范围,消息传输转换为在分割区域间的传输,一定程度上降低了算法的计算量,同时利用图像分割的约束提高了算法对遮挡区域和缺少纹理区域的处理能力.实验结果表明,算法相对于基于像素点的置信传播算法计算量减小,匹配精度高,对缺少纹理区域和遮挡区域的处理更有优势,得到了较准确的稠密视差图.     

基于高斯混合模型的纹理图像的分割

文章提出了使用高斯混合模型对纹理图像分割的方法。该模型考虑到图像纹理的特点，使用二状态的高斯混合分布来对图像各纹理类像素的分布进行拟和。首先对待分割的各类像素寻找与之匹配的最佳模型参数，然后再进行分割。最后，对分割结果进行多尺度综合，得到了各尺度的分割结果。实验表明该算法不仅能够较精确地定位各纹理区域，而且还有运算速度快的优点。     

Image Segmentation Using Excess Entropy

We present a novel information-theoretic approach for thresholding-based segmentation that uses the excess entropy to measure the structural information of a 2D or 3D image and to locate the optimal thresholds. This approach is based on the conjecture that the optimal thresholding corresponds to the segmentation with maximum structure, i.e., maximum excess entropy. The contributions of this paper are several fold. First, we introduce the excess entropy as a measure of the spatial structure of an image. Second, we present an adaptive thresholding method based on the maximization of excess entropy. Third, we propose the use of uniformly distributed random lines to overcome the main drawbacks of the excess entropy computation. To show the good performance of the proposed segmentation approach different experiments on synthetic and real brain models are carried out.     

基于自组织网络的SAR遥感图像的多尺度分割

基于多尺度信息特征和混合模型,将自组织混合网络(SOMN)应用于合成孔径雷达(SAR)图像的分割.首先对SAR图像的多尺度序列进行多尺度随机建模,以此进行多尺度特征提取;然后对其建立混合模型,并经过SOMN进行学习研究得到混合模型的参数;最后再利用Bayesian分类器,对SAR图像进行分割.实验结果表明,本文方法能够充分地利用SAR图像多尺度序列中不同类型地形的统计信息,进而明显地改进了图像的分割质量.     

Medical Image Segmentation Using Genetic Algorithms

Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.      

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.     

一种新的自动多阈值图像分割方法

本文提出了一种新的自动阈值图像分割方法.该方法选择二维灰度直方图的最佳一维投影作为输入信息,并利用了小波变换的多尺度特征分析此投影.在大尺度下抑制噪声并自动确定分割类数,在小尺度下精确定位阈值.实验表明新方法能够自动而快速地对图像进行分割而且有较强的抗干扰能力.     

用可变形模板进行基于内容的图像分割算法

 本文提出一种采用可变形模板匹配技术进行基于内容的图像分割算法.通过预先计算出可变形模板沿着变形的正交曲线,并对模板曲线及正交曲线进行离散抽样,建立一基于正交曲线的二维(2-D)可变形模板,针对图像分割问题定义控制可变形模板进行变形的内、外部能量函数,本文采用遗传算法搜索能量函数最小的全局最优解.该新算法比传统的可变形模板匹配方法降低了搜索空间的维数,减少了算法对模板初始位置的敏感.对实际图像及模拟低信噪比图像处理的结果表明,新算法具有良好的分割精度及稳定性.     

Preferential Image Segmentation Using Trees of Shapes

A novel preferential image segmentation method is proposed that performs image segmentation and object recognition using mathematical morphologies. The method preferentially segments objects that have intensities and boundaries similar to those of objects in a database of prior images. A tree of shapes is utilized to represent the content distributions in images, and curve matching is applied to compare the boundaries. The algorithm is invariant to contrast change and similarity transformations of translation, rotation and scale. A performance evaluation of the proposed method using a large image dataset is provided. Experimental results show that the proposed approach is promising for applications such as object segmentation and video tracking with cluttered backgrounds.      

一种利用图像角点和边缘信息的阈值分割方法

阈值化算法是图像区域分割的一个基本算法.提出一种利用角点和边缘点的像素灰度值均值和全图像像素点灰度值均值加权相加的阈值选取方法.实验表明,该方法能对具有大面积目标的图像进行很好的分割.     

MR Image Segmentation Using a Power Transformation Approach

This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter) resulting from the method were validated and evaluated against manual segmentation results based on 38 real T1-weighted image volumes from the internet brain segmentation repository, and 18 simulated image volumes from BrainWeb. The Jaccard and Dice similarity indexes were computed to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional segmentation using the Gaussian mixtures approach.      

一种非参数估计的活动围道图像分割方法

该文采用Parzen窗非参数估计的活动围道方法进行图像分割,不必假定图像区域符合特定的概率分布,大大增加了该算法的应用范围。通过动态调整Parzen窗口参数,更好地利用了图像灰度区域一致的信息,使之更好地适应图像分割的任务。通过合成图像和真实图像的实验证明此方法具有很好的合理性,具有很好的分割精度和抗噪性能。     

采用全局相似性测量的彩色图像分割

本文提出一种新的采用全局相似性测量的彩色图像分割算法。该算法将图像分割问题表述为求解能量泛函最小化的问题。利用Bhattacharyya距离测量前景和背景之间的概率分布函数的全局相似性,并将Bhattacharyya系数作为最终的能量泛函。由于高阶能量项的引入,该能量的最小化通常是一个NP难题,为了能够有效的优化该能量泛函,本文提出一个辅助上界函数并且利用graph cuts对该函数进行优化求解。该辅助函数在优化的过程中能够保证能量递减。该算法能够应用于多种分割问题,包括交互式分割、显著性分割等。实验结果表明,算法具有全局分割的特点,能够对彩色图像进行较准确的分割。      

基于SVM能量模型的改进主动轮廓图像分割算法研究

为克服经典主动轮廓模型曲线内外区域能量定义在复杂目标与背景分布情况下的不足,本文将高效的支持向量机有监督学习分类器引入基于Mumford-shah模型的主动轮廓图像分割算法中,提出了基于SVM能量模型的改进主动轮廓图像分割方法.该方法首先利用支持向量机的分类结果对于封闭曲线的内外区域分别构造了一种新的图像能量表示方法,因为分割过程充分利用了有监督学习策略,使得本文提出的算法具有更高的稳定性和更加广泛的适用范围,特别是对目标灰度分布不均或存在多纹理的目标也可以得到较好的分割结果.分割时,首先利用SVM实现粗分割得到目标初始轮廓,然后利用改进的Mumford-shah主动轮廓模型进行精确分割,采用粗分割策略一方面可以大大提高分割速度,另一方面也可以提高了算法的自动化程度.对比实验结果表明本文提出的算法具有更大灵活性和更好的分割性能.     

隐式曲面两相图像分割的变分水平集模型及对偶方法

本文对曲面上分段常值和分段光滑的两相图像分割的变分水平集模型及其对偶方法进行了研究.图像所在的曲面用静态的符号距离函数的零水平集表达,曲面上图像分割轮廓线用另一动态符号距离函数的零水平集与上述静态的零水平集的交线表达,借助内蕴梯度、内蕴散度的概念平面两相图像分割的变分水平集模型已被推广到隐式曲面上图像分割的变分模型.本...     

利用RJMCMC算法的可变类SAR图像分割

自动确定地物类别数是SAR图像分割方法研究的重点和难点问题,为此,提出一种自动确定类别数的SAR图像分割算法。首先假定SAR图像中各像素强度服从同一独立的Gamma分布并以此建立图像模型;根据贝叶斯定理构建刻画图像分割的后验概率模型;设计RJMCMC（Reversible Jump Markov Chain Monte Carlo）算法模拟该后验概率模型,以确定图像类别数并同时完成区域分割。在提出的RJMCMC算法中,设计的移动操作类型包括:分裂或合并实类、改变参数矢量、改变标号及生成或删除空类。为了验证提出的可变类分割算法,分别对真实及模拟SAR图像进行可变类分割实验,定性及定量精度评价结果表明该算法的可行性及有效性。