彩色图像序列中运动目标外轮廓的自动提取

该文提出了一种新的多运动目标精确外轮廓自动提取算法。算法的主要创新点是:采用指数滤波计算彩色图像的梯度强度,改善了彩色图像梯度局部极值点的定位精度,所得到的梯度强度和运动边缘位置信息供随后的轮廓迭代中使用,成功地避免了动态轮廓的过收缩。此外,充分利用轮廓的方向信息,又有效地克服了动态轮廓迭代中的外扩展。从而,算法自动地提取出一个真正紧贴的目标外轮廓。分析和实验表明,该算法抗干扰能力强,对复杂背景及不重叠物体,可以精确定位并提取出各个运动目标的外轮廓。     

Adaptive pseudo dilation for gestalt edge grouping and contour detection

We consider the problem of detecting object contours in natural images. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. We introduce a new morphological operator, called adaptive pseudo-dilation (APD), which uses context dependent structuring elements in order to identify long curvilinear structure in the edge map. We show that grouping edge pixels as the connected components of the output of APD results in a good agreement with the gestalt law of good continuation. The novelty of this operator is that dilation is limited to the Voronoi cell of each edge pixel. An efficient implementation of APD is presented. The grouping algorithm is then embedded in a multithreshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. The use of different thresholds makes the algorithm much less sensitive to the values of the input parameters. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contours.     

基于Corvis ST医学影像的角膜轮廓提取算法

目前已有的轮廓提取算法在提取可视化角膜生物力学分析仪(corneal visualization scheimpflug technology, Corvis ST)影像的角膜轮廓中,由于角膜边缘的局部图像灰度分布相近这一特点,提取出的角膜轮廓普遍不完整或者提取的角膜轮廓边缘出现细小突出。这会使得角膜轮廓的完整性遭到破坏,提取到的角膜轮廓与实际的角膜原始图像严重不符。本文针对Corvis ST采集的角膜图像的轮廓提取问题,基于最大类间方差法(OTSU)算法设计一种高效的图像处理方法。首先,将角膜图像进行除杂、灰度化以及图像降噪等处理,达到减少图像计算量和降低数字图像噪声干扰的目的;其次,基于OTSU算法对图像进行分割,并在此基础上加入数学形态学运算,达到平滑图像边界和填充细小“孔洞”的目的;最后,采用Canny边缘检测算法提取图像中的角膜轮廓,达成提取出高完整性角膜轮廓的目的。在相同的图像数据集上,与最新的纽扣轮廓瑕疵检测系统中设计的轮廓提取算法(B-OTSU algorithm)进行了对比实验。实验结果表明,从轮廓完整性以及准确性的角度,应用本文方法提取的角膜轮廓明显优于最新的纽扣轮...     

Automatic contour propagation in cine cardiac magnetic resonance images

We have developed a method for automatic contour propagation in cine cardiac magnetic resonance images. The method consists of a new active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours should maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, we describe how to use full factorial experiments to optimize the parameter setting. We have applied our method to cine cardiac magnetic resonance image sequences from the long axis two-chamber view, the long axis four-chamber view, and the short axis view. We performed our optimization procedure for each contour in each view. Next, we performed an extensive clinical validation of our method on 69 short axis data sets and 38 long axis data sets. In the optimal parameter setting, our propagation method proved to be fast, robust, and accurate. The resulting cardiac contours are positioned within the interobserver ranges of manual segmentation. Consequently, the resulting contours can be used to accurately determine physiological parameters such as stroke volume and ejection fraction.     

Contour extraction from cardiac MRI studies using snakes

The author investigated automatic extraction of left ventricular contours from cardiac magnetic resonance imaging (MRI) studies. The contour extraction algorithms were based on active contour models, or snakes. Based on cardiac MR image characteristics, the author suggested algorithms for extracting contours from these large data sets. The author specifically considered contour propagation methods to make the contours reliable enough despite noise, artifacts, and poor temporal resolution. The emphasis was on reliable contour extraction with a minimum of user interaction. Both spin echo and gradient echo studies were considered. The extracted contours were used for determining quantitative measures for the heart and could also be used for obtaining graphically rendered cardiac surfaces.     

Segmentation and tracking in echocardiographic sequences: activecontours guided by optical flow estimates

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability     

利用轮廓特征识别人的日常行为

提出了一种以人的动作序列图像的轮廓为特征,基于RBF神经网络的日常行为识别方法.首先通过建立自适应背景模型分割出动作序列并提取轮廓,然后利用傅立叶描述子处理动作的轮廓线序列并进行数据维数压缩,结合径向基神经网络进行行为识别.实验表明,利用本方法对人的日常行为的正确识别率达到90%以上,具有简单、实用的特点.     

STACS: new active contour scheme for cardiac MR image segmentation

The paper presents a novel stochastic active contour scheme (STACS) for automatic image segmentation designed to overcome some of the unique challenges in cardiac MR images such as problems with low contrast, papillary muscles, and turbulent blood flow. STACS minimizes an energy functional that combines stochastic region-based and edge-based information with shape priors of the heart and local properties of the contour. The minimization algorithm solves, by the level set method, the Euler-Lagrange equation that describes the contour evolution. STACS includes an annealing schedule that balances dynamically the weight of the different terms in the energy functional. Three particularly attractive features of STACS are: 1) ability to segment images with low texture contrast by modeling stochastically the image textures; 2) robustness to initial contour and noise because of the utilization of both edge and region-based information; 3) ability to segment the heart from the chest wall and the undesired papillary muscles due to inclusion of heart shape priors. Application of STACS to a set of 48 real cardiac MR images shows that it can successfully segment the heart from its surroundings such as the chest wall and the heart structures (the left and right ventricles and the epicardium.) We compare STACS' automatically generated contours with manually-traced contours, or the "gold standard," using both area and edge similarity measures. This assessment demonstrates very good and consistent segmentation performance of STACS.     

A contour-based approach to multisensor image registration

Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral/multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. The authors present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain-code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, the authors propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of the algorithms, which have outperformed manual registration in terms of root mean square error at the control points.     

A discrete dynamic contour model

A discrete dynamic model for defining contours in 2-D images is developed. The structure of this model is a set of connected vertices. With a minimum of interaction, an initial contour model can be defined, which is then automatically modified by an energy minimizing process. The internal energy of the model depends on local contour curvature, while the external energy is derived from image features. Solutions are presented to avoid undesirable deformation effects, like shrinking and vertex clustering, which are common in existing active contour models. The deformation process stops when a local minimum of the energy function is reached. The final shape of the model is a reproducible approximation of the desired contour. Results of applying the method to computer-generated images, as well as clinical images, are presented.     

基于局部灰度分析的红外图像轮廓跟踪算法

针对在红外图像边缘提取时,断边和分支点会导致伪边界的问题,提出了一种带搜索控制准则的红外图像轮廓跟踪算法.该算法根据飞行目标红外图像特点,将基于最大距离法的局部阈值分割用于目标图像的提取.给出了关于图像局部灰度特性的3个推论,并由此将遗传算法用于轮廓跟踪过程,自动判断出轮廓跟踪过程中出现的分支点、断边,搜索断边并连接.实验结果表明,采用该算法可得到清晰、光滑的轮廓图像,并且具有较快的运算速度,可实现实时处理.     

Matching shapes with self-intersections:application to leaf classification

We address the problem of two-dimensional (2-D) shape representation and matching in presence of self-intersection for large image databases. This may occur when part of an object is hidden behind another part and results in a darker section in the gray level image of the object. The boundary contour of the object must include the boundary of this part which is entirely inside the outline of the object. The Curvature Scale Space (CSS) image of a shape is a multiscale organization of its inflection points as it is smoothed. The CSS-based shape representation method has been selected for MPEG-7 standardization. We study the effects of contour self-intersection on the Curvature Scale Space image. When there is no self-intersection, the CSS image contains several arch shape contours, each related to a concavity or a convexity of the shape. Self intersections create contours with minima as well as maxima in the CSS image. An efficient shape representation method has been introduced in this paper which describes a shape using the maxima as well as the minima of its CSS contours. This is a natural generalization of the conventional method which only includes the maxima of the CSS image contours. The conventional matching algorithm has also been modified to accommodate the new information about the minima. The method has been successfully used in a real world application to find, for an unknown leaf, similar classes from a database of classified leaf images representing different varieties of chrysanthemum. For many classes of leaves, self-intersection is inevitable during the scanning of the image. Therefore the original contributions of this paper is the generalization of the Curvature Scale Space representation to the class of 2-D contours with self-intersection, and its application to the classification of Chrysanthemum leaves.     

一种用于形状描述的拱高半径复函数

提出了一种新的用于形状描述的轮廓线函数-拱高半径复函数(AHRC).AHRC用中心距离和带正负号的拱高来分别描述形状的全局特征和局部细节.用AHRC的傅立叶变换系数构成描述形状的特征向量.在MPEG-7标准测试集上对该方法进行图像检索实验,并将其实际应用于植物叶片图像的检索,同现有的分别基于中心距离、三角形面积、最远点...     

An Active Contour Algorithm for Continuous-Time Cellular Neural Networks

A CNN-based algorithm for image segmentation by active contours is proposed here. The algorithm is based on an iterative process of expansion of the contour and its subsequent thinning guided by external and internal energy. The proposed strategy allows for a high level of control over contour evolution making their topologic transformations easier. Therefore processing of multiple contours for segmenting several objects can be carried out simultaneously.     

Fully automatic luminal contour segmentation in intracoronary ultrasound imaging--a statistical approach

In this paper, a fully automatic method for luminal contour segmentation in intracoronary ultrasound imaging is introduced. Its principle is based on a contour with a priori properties that evolves according to the statistics of the ultrasound texture brightness, which is generally Rayleigh distributed. The main interest of the technique is its fully automatic character. This is insured by an initial contour that is not set by the user, like in classical snake-based algorithms, but estimated and, thus, adapted to each image. Its estimation combines two pieces of information extracted from the a posteriori probability function of the contour position: the function maximum location (or maximum a posteriori estimator) and the first zero-crossing of its derivative. Then, starting from the initial contour, a region of interest is automatically selected and the process iterated until the contour evolution can be ignored. In vivo coronary images from 15 patients, acquired with the 20-MHz central frequency Jomed Invision ultrasound scanner, were segmented with the developed method. Automatic contours were compared to those manually drawn by two physicians in terms of mean absolute difference. The results demonstrate that the error between automatic contours and the average of manual ones is of small amplitude, and only very slightly higher (0.099 +/- 0.032 mm) than the interexpert error (0.097 +/- 0.027 mm).