A snake for model-based segmentation of biomedical images

In this work we present a snake based approach for the segmentation of images of computerized tomography (CT) scans. We introduce a new term for the internal energy and another one for external energy which solve common problems associated with classical snakes in this type of images. A simplified minimizing method is also presented.     

基于小波模糊聚类的均质纹理和非均质纹理图象检索

基于内容的图象检索是近年来的研究热点 ,为此提出了一种自动区分均质纹理和非均质纹理图象 ,并对这两类图象分别进行检索的算法 .算法首先从图象离散小波变换的低频子带提取一定的颜色和纹理特征用于模糊聚类 ,将图象的低频子带分割为一定的区域 ;然后根据分割的结果将图象自动语义分类为均质纹理或者非均质纹理图象 ;最后对均质纹理和非均质纹理图象分别提取不同的特征矢量 ,并按照一定的相似度准则检索图象 .实验结果表明 ,该算法具有良好的均质纹理和非均质纹理图象分类和检索性能 .     

SQR: a simple quantum representation of infrared images

A simple quantum representation (SQR) of infrared images is proposed based on the characteristic that infrared images reflect infrared radiation energy of objects. The proposed SQR model is inspired from the Qubit Lattice representation for color images. Instead of the angle parameter of a qubit to store a color as in Qubit Lattice representation, probability of projection measurement is used to store the radiation energy value of each pixel for the first time in this model. Since the relationship between radiation energy values and probability values can be quantified for the limited radiation energy values, it makes the proposed model more clear. In the process of image preparation, only simple quantum gates are used, and the performance comparison with the latest flexible representation of quantum images reveals that SQR can achieve a quadratic speedup in quantum image preparation. Meanwhile, quantum infrared image operations can be performed conveniently based on SQR, including both the global operations and local operations. This paper provides a basic way to express infrared images in quantum computer.     

基于LGDF模型的医学图像分割及有偏场校正

为实现对灰度不均匀医学图像分割的同时进行有偏场估计并校正,改进了基于局部高斯分布拟合(Local Gaussian Distribution Fitting,LGDF)能量的活动轮廓模型。通过分析图像有偏场模型的局部特性,将有偏场乘性因子引入图像局部灰度均值的表达中,从而使有偏场乘性因子成为新的能量函数的变量。能量函数的迭代最小化既实现了目标组织分割,又有效估计了有偏场。合成图像和真实医学图像实验表明该方法比现有多种方法分割性能更好,且利用估计的有偏场校正后的图像具有更好的视觉效果。     

基于颜色与纹理特征的局部主动轮廓模型

针对图像分割过程中前背景表面特征不均质问题,提出一种基于颜色纹理先验特征的多通道局部能量模型。对现有局部能量模型进行扩展,降低初始轮廓线位置对分割结果的影响,并引入8维HSV颜色模型和变换域结构张量纹理特征,实现前背景颜色特性相似的图像分割。实验结果表明,该模型具有较好的分割效果。     

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.     

Unsupervised refinement of color and stroke features for text binarization

Color and strokes are the salient features of text regions in an image. In this work, we use both these features as cues, and introduce a novel energy function to formulate the text binarization problem. The minimum of this energy function corresponds to the optimal binarization. We minimize the energy function with an iterative graph cut-based algorithm. Our model is robust to variations in foreground and background as we learn Gaussian mixture models for color and strokes in each iteration of the graph cut. We show results on word images from the challenging ICDAR 2003/2011, born-digital image and street view text datasets, as well as full scene images containing text from ICDAR 2013 datasets, and compare our performance with state-of-the-art methods. Our approach shows significant improvements in performance under a variety of performance measures commonly used to assess text binarization schemes. In addition, our method adapts to diverse document images, like text in videos, handwritten text images.     

约束方程能量最小化提取3维血管图像中轴线

3维血管中轴线提取是血管量化的第一步,同时也是最重要的一步。为此,提出一种动态提取3维血管图像中轴线的方法。首先构造具有3维血管特征的能量约束方程,通过细化方法或人工构造方式得到血管图像初始骨架线,在能量约束方程的作用下,沿着血管图像距离场梯度的方向不断向血管中轴线位置逼近。当方程能量达到最小值时,初始骨架线也就固定在中轴线位置。实验结果表明,提取出来的血管中轴线位置准确,且保持拓扑结构和连通性。     

A Context Sensitive Multilevel Thresholding Using Swarm Based Algorithms

In this paper, a comprehensive energy function is used to formulate the three most popular objective functions: Kapur's, Otsu and Tsalli's functions for performing effective multilevel color image thresholding. These new energy based objective criterions are further combined with the proficient search capability of swarm based algorithms to improve the efficiency and robustness. The proposed multilevel thresholding approach accurately determines the optimal threshold values by using generated energy curve, and acutely distinguishes different objects within the multi-channel complex images. The performance evaluation indices and experiments on different test images illustrate that Kapur's entropy aided with differential evolution and bacterial foraging optimization algorithm generates the most accurate and visually pleasing segmented images.      

Unsupervised multi-class segmentation of SAR images using triplet Markov fields models based on edge penalty

Non-Gaussian triplet Markov random fields (TMF) model is suitable for dealing with multi-class segmentation of nonstationary and non-Gaussian synthetic aperture radar (SAR) images. However, the segmentation of SAR images utilizing this model still fails to resolve the misclassifications due to the inaccuracy of edge location. In this paper, we propose a new unsupervised multi-class segmentation algorithm by fusing the traditional energy function of TMF model with the principle of edge penalty. Through the introduction of the penalty function based on local edge strength information, the new energy function could prevent segment from smoothing across boundaries. Then we optimize the objective function that stems from the new energy function to obtain an iterative multi-region merging Bayesian maximum posterior mode (MPM) segmentation equation for the new segmentation algorithm. The effectiveness of the proposed algorithm is demonstrated by application to simulated data and real SAR images.     

An Accurate and Fast Pattern Localization Algorithm for Automated Visual Inspection

Accurate and efficient localization of patterns from noisy images is very crucial in automated visual inspection. In this paper we present an accurate, efficient and robust algorithm for 2D pattern localization based on a modified optical flow constraint, which is derived from the first-order Taylor series approximation of the generalized brightness assumption. Our algorithm allows for large global illumination changes since this factor is explicitly modeled on the generalized brightness assumption. The proposed pattern localization algorithm is based on an energy minimization approach, with the energy function being defined as a weighted sum of the modified optical flow constraints at selected locations with reliable constraints. This location selection is facilitated by a reliability measure given in this paper. The energy minimization is accomplished via an efficient Newton-like iterative algorithm, which has been proved to be reliable for precise localization problems with a rough initial guess from our experiments. Experimental results on some real images and the corresponding simulated images are given to demonstrate the accuracy, efficiency and robustness of our algorithm.     

基于图像块能量梯度的多聚焦图像融合

针对多聚焦图像,提出一种基于图像分块的融合方法。将源图像分为大小相同数量相等的子块,采用能量梯度算子作为对焦评价函数,计算各个图像子块能量梯度匹配度,设置匹配度阈值分离出源图像中的清晰区域。源图像中的清晰区域直接作为融合图像相应的区域,其它区域的处理中,构造与相应子块能量梯度大小相关的图像序列,以及像素点到各个子块中心距离相关的融合函数,然后用融合函数对图像序列融合。实验结果表明该方法有效性和合理性。     

基于亚像素位移的超分辨率图像重建算法

针对多帧图像超分辨率重建问题, 利用一阶泰勒展式, 在亚像素级上对图像退化过程进行建模, 并建立极小化能量函数, 选择Graph-cut算法进行能量极小化求解. 为了验证本文算法的有效性, 采用模拟图像退化过程和直接用相机拍摄两种方式获得低分辨率图像序列. 从4×4倍重建结果的比较来看, 本文算法不仅对模拟退化过程产生的低分辨率图像序列有效, 而且在提高真实低分辨率图像的分辨能力方面也有很好的效果. 此外, 实验结果表明本文算法对噪声有较好的抗干扰能力.     

结合张量投票和Snakes模型的SAR图像道路提取

目的 Snakes模型对曲线轮廓具有良好的拟合能力,被广泛应用于遥感图像的道路提取。但SAR图像受乘性斑点噪声影响严重,因此利用Snakes模型从SAR图像提取道路时,传统的以图像灰度负梯度为外部能量的方法难以取得理想结果。针对这一问题,利用计算机视觉中的张量投票算法可以从噪声掩盖的图像中提取显著结构特征的特点,将张量投票与Snakes模型结合从SAR图像提取道路。方法 首先利用模糊C均值分割法从SAR图像中分割出道路类,然后对道路类进行张量投票获得每点的曲线显著性值,最后以该曲线显著性值的负值作为Snakes模型外部能量从SAR图像提取道路。在Snakes模型能量最小化阶段,提出了一种优化的拟合策略,一边内插节点一边最小化Snakes模型能量。结果 利用机载和星载不同场景的SAR图像进行实验,与同类的基于Snakes模型的半自动方法相比,本文方法对曲率较大的道路仅需较少控制点即可取得较好的拟合效果;与基于MRF模型的自动方法相比,本文方法对道路提取的完整率、正确率、检测质量都优于基于MRF模型的方法,并且提取的时间远远快于基于MRF模型的方法,对于大范围的道路网提取将更为实用。结论 本文方法充分考虑到道路的几何形态特征,利用张量投票算法对该特征进行量化,并利用优化的拟合策略来最小化Snakes模型能量来提取道路。基于机载和星载SAR图像的实验表明本文方法可以较好地提取不同场景中的主要道路目标和道路网。     

基于模板匹配和遗传算法的人眼定位

文中提出了一种利用模板匹配与遗传算法相结合的人眼定位算法.根据人脸几何特征将人脸分为几个特征区域(眼睛、鼻子、嘴巴),在找到人眼区域后根据人眼几何特征,建立人眼特征模板及其能量函数,通过改变模型参数用遗传算法实现能量函数的全局最小化,使能量函数最小的模型参数即可描述人眼的特征,从而实现人脸特征的有效定位.同时还讨论了不同的能量函数在人眼定位过程中的地位和作用.可避免在能量函数寻优过程中的局部最小化的出现,较准确较快捷的确定人脸主要特征的位置.     

Ellipse Motion Estimation Using Parametric Snakes

In this paper we propose a multiscale parametric snake model for ellipse motion estimation across a sequence of images. We use a robust ellipse parameterization based on the geometry of the intersection of a cylinder and a plane. The ellipse parameters are optimized in each frame by searching for local minima of the snake model energy including temporal coherence in the ellipse motion. One advantage of this method is that it just considers the convolution of the image with a Gaussian kernel and its gradient, and no edge detection is required. A detailed study about the numerical evaluation of the snake energy on ellipses is presented. We propose a Newton–Raphson-type algorithm to estimate a local minimum of the energy. We present some experimental results on synthetic data, real video sequences and 3D medical images.     

基于Contourlet能量标准差积的极化SAR图像融合

针对Contourlet多尺度、多方向性的优点,以及单一特征量融合规则过于片面性的缺点,提出了一种结合Contourlet自适应阈值滤波的区域能量标准差积的多极化SAR图像融合算法.该方法利用Sigmoid函数构建一种自适应阈值函数来处理Contourlet的高频子带系数,实现融合前图像的去噪处理,然后在Contourlet域中完成不同极化SAR图像的信息融合.根据各子带系数的特性,对低频子带系数采用区域能量融合规则和加权算法;高频子带系数采用区域能量和标准差之积作为融合规则,进行选择性融合.通过对实测极化SAR图像融合的试验表明,该算法在目视效果和客观评价指标方面比其他算法,都具有一定的优越性.     

局部高斯分布拟合的脑MR图像分割及有偏场校正

为实现对灰度不均匀脑核磁共振(MR)图像分割的同时进行有偏场估计并校正,提出一种基于局部高斯分布拟合(LGDF)模型的多相水平集方法.通过分析图像有偏场模型的局部特性,将有偏场乘性因子引入到图像局部灰度均值的表达中,从而使有偏场乘性因子成为新的能量函数的变量.能量函数的迭代最小化既实现了目标组织分割,又有效估计了有偏场.合成图像和仿真脑MR图像实验结果表明,本文方法比现有多种方法分割性能更好,且利用本文方法估计的有偏场校正后的图像有更好的视觉效果.     

Probability density difference-based active contour for ultrasound image segmentation

Because of its low signal/noise ratio, low contrast and blurry boundaries, ultrasound (US) image segmentation is a difficult task. In this paper, a novel level set-based active contour model is proposed for breast ultrasound (BUS) image segmentation. At first, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. The actual probability densities are calculated directly. For calculating the estimated probability densities, the probability density estimation method and background knowledge are utilized. The energy function is formulated with level set approach, and a partial differential equation is derived for finding the minimum of the energy function. For performing numerical computation, the derived partial differential equation is approximated by the central difference and non-re-initialization approach. The proposed method was operated on both the synthetic images and clinical BUS images for studying its characteristics and evaluating its performance. The experimental results demonstrate that the proposed method can model the BUS images well, be robust to noise, and segment the BUS images accurately and reliably.     

A Coupled Minimization Problem for Medical Image Segmentation with Priors

We present a coupled minimization problem for image segmentation using prior shape and intensity profile. One part of the model minimizes a shape related energy and the energy of geometric active contour with a parameter that balances the influence from these two. The minimizer corresponding to a fixed parameter in this minimization gives a segmentation and an alignment between the segmentation and prior shape. The second part of this model optimizes the selection of the parameter by maximizing the mutual information of image geometry between the prior and the aligned novel image over all the alignments corresponding to different parameters in the first part. By this coupling the segmentation arrives at higher image gradient, forms a shape similar to the prior, and captures the prior intensity profile. We also propose using mutual information of image geometry to generate intensity model from a set of training images. Experimental results on cardiac ultrasound images are presented. These results indicate that the proposed model provides close agreement with expert traced borders, and the parameter determined in this model for one image can be used for images with similar properties.