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     

Spatio-temporal segmentation of image sequences for object-oriented low bit-rate image coding

This paper describes a new method of segmentation of time-varying image sequences whose goal is object-oriented image coding. The segmentation represents a partition of each frame of the sequence into a set of regions which are homogeneous with regard to motion criterion. The region borders correspond to spatial contours of objects in the frame. Each spatio-temporal region is characterized by its temporal component, which is a model-dependent vector of motion parameters, and a structural component representing the polygonal approximation of the spatial contour of the region.

The construction of spatio-temporal segmentation includes two phases: the initialization step and temporal tracking. The initialization step is based on the spatial segmentation of the first frame of the sequence. Then homogeneous spatial regions are merged through motion estimation in accordance with a motion-based criterion. The temporal tracking consists of the projection of the segmentation along the time axis, and its adjustment. Special attention is paid to the processing of occlusions.

A predictive coding scheme is proposed which is based on the temporal coherence of the segmentation. This scheme is promising for a low bit-rate image compression.

The results for teleconference and TV sequences show the high visual quality of reconstructed only by prediction images. Moreover, the bit-rates for motion coding are very low: from 0.002 to 0.007 bit/pixel for teleconference sequence and from 0.004 to 0.021 bit/pixel for complex TV sequence. A scheme for encoding of the structural information is proposed which requires 0.083 – 0.17 bit per pixel depending on the content of the sequence.     

An interactive authoring system for video object segmentation and annotation

An interactive authoring system is proposed for semi-automatic video object (VO) segmentation and annotation. This system features a new contour interpolation algorithm, which enables the user to define the contour of a VO on multiple frames while the computer interpolates the missing contours of this object on every frame automatically. Typical active contour (snake) model is adapted and the contour interpolation problem is decomposed into a two-directional contour tracking problem and a merging problem. In addition, new user interaction models are designed for the user to interact with the computer. Experiments indicate that this system offers a good balance between algorithm complexity and user interaction efficiency.     

Tracking myocardial deformation using phase contrast MR velocity fields: a stochastic approach

The authors propose a new approach for tracking the deformation of the left-ventricular (LV) myocardium from two-dimensional (2-D) magnetic resonance (MR) phase contrast velocity fields. The use of phase contrast MR velocity data in cardiac motion problems has been introduced by others (N.J. Pelc et al., 1991) and shown to be potentially useful for tracking discrete tissue elements, and therefore, characterizing LV motion. However, the authors show here that these velocity data: 1) are extremely noisy near the LV borders; and 2) cannot alone be used to estimate the motion and the deformation of the entire myocardium due to noise in the velocity fields. In this new approach, the authors use the natural spatial constraints of the endocardial and epicardial contours, detected semiautomatically in each image frame, to help remove noisy velocity vectors at the LV contours. The information from both the boundaries and the phase contrast velocity data is then integrated into a deforming mesh that is placed over the myocardium at one time frame and then tracked over the entire cardiac cycle. The deformation is guided by a Kalman filter that provides a compromise between 1) believing the dense field velocity and the contour data when it is crisp and coherent in a local spatial and temporal sense and 2) employing a temporally smooth cyclic model of cardiac motion when contour and velocity data are not trustworthy. The Kalman filter is particularly well suited to this task as it produces an optimal estimate of the left ventricle's kinematics (in the sense that the error is statistically minimized) given incomplete and noise corrupted data, and given a basic dynamical model of the left ventricle. The method has been evaluated with simulated data; the average error between tracked nodes and theoretical position was 1.8% of the total path length. The algorithm has also been evaluated with phantom data; the average error was 4.4% of the total path length. The authors show that in their initial tests with phantoms that the new approach shows small, but concrete improvements over previous techniques that used primarily phase contrast velocity data alone. They feel that these improvements will be amplified greatly as they move to direct comparisons in in vivo and three-dimensional (3-D) datasets.     

基于Snake活动轮廓模型的视频跟踪分割方法

基于Snake活动轮廓模型,采用时空融合的方式,根据短时间内相邻帧的运动趋势差异相似的前提,首先将视频序列分成若干个小段,每段有k帧视频,选取段内的前两帧为关键帧,通过运动检测的方式自动得到这两帧中运动对象的大致区域;然后进行帧内Snake演变,搜索精确轮廓;最后以关键帧间运动对象形心的运动矢量预测勾勒后续帧的初始轮廓,进行帧内Snake精确轮廓定位,从而实现所有帧的视频对象分割。相比于传统方法,本文方法克服了手动绘制初始轮廓的缺点,在空域对Snake贪婪方法进行了改进而且精确度高,速度快。实验表明,本文方法成功地实现了前后帧图像之间运动对象的对应匹配关系,并通过改进后的Snake贪婪方法得到了精确的分割结果。     

Wall position and thickness estimation from sequences of echocardiographic images

Presents a new method for endocardial (inner) and epicardial (outer) contour estimation from sequences of echocardiographic images. The framework herein introduced is fine-tuned for parasternal short axis views at the papillary muscle level. The underlying model is probabilistic; it captures the relevant features of the image generation physical mechanisms and of the heart morphology. Contour sequences are assumed to be two-dimensional noncausal first-order Markov random processes; each variable has a spatial index and a temporal index. The image pixels are modeled as Rayleigh distributed random variables with means depending on their positions (inside endocardium, between endocardium and pericardium, or outside pericardium). The complete probabilistic model is built under the Bayesian framework. As estimation criterion the maximum a posteriori (MAP) is adopted. To solve the optimization problem, one is led to (joint estimation of contours and distributions' parameters), the authors introduce an algorithm herein named iterative multigrid dynamic programming (IMDP). It is a fully data-driven scheme with no ad-hoc parameters. The method is implemented on an ordinary workstation, leading to computation times compatible with operational use. Experiments with simulated and real images are presented.     

基于复杂场景下特征融合与估计的目标跟踪算法

针对图像跟踪过程中的背景干扰、光照条件变化与目标特性漂移等问题,提出一种粒子滤波框架下灰度和纹理特征融合与估计的目标跟踪算法.首先建立基于灰度直方图和边缘梯度直方图的特征集合,以增加目标描述的信息量和稳定性;然后在跟踪过程中依据每个特征对目标与背景的区分度赋予其相应的权值,突出贡献较大的特征,并将该加权系数用于目标和候选目标的相似性度量;最后,为了克服复杂场景对单幅图像中特征可信度的影响,将跟踪的连续性引入权值的自适应调整过程,利用当前帧的特征区分度和前一帧的特征权值对当前帧的特征权值进行估计,从而实现特征权值的在线更新.实验结果表明,通过对特征的融合及其权值的在线估计,实现了复杂场景下的稳定目标跟踪.     

基于水平集算法后向散射的运动目标轮廓提取

基于距离选通的激光主动成像系统在大气条件下对运动目标成像时,为了在更大范围内搜索跟踪目标,距离选通初始门宽通常在微秒量级,进而引起后向散射,严重影响了系统对目标的识别与提取。文中首先分析了基于距离选通的激光主动成像系统,在此基础上引入距离信息实时跟踪运动目标,研究了不同选通门宽引起的后向散射对图像质量的影响,提出了一种基于Sobel 算子和 GAC 模型的目标轮廓提取算法,将 Sobel 边缘检测的结果引入水平集边界停止函数,为水平集的曲线演化提供参考依据。实验结果表明,提出的水平集算法能够克服严重后向散射干扰影响提取目标轮廓,目标提取精度在0.9以上,高于 GAC、LIF、LBF 等经典水平集算法。     

Hierarchical optimal contour control of motion systems

Many motion control applications utilize multiple axes to traverse complex trajectories. The hierarchical contour control methodology proposed in this paper treats each axis as an individual subsystem and combines the Internal Model Principle with robust tracking and optimal hierarchical control techniques to track a desired trajectory. In this method the objectives are divided into two levels. Measurable goals of each subsystem are included in the bottom level and unmeasurable goals, which are estimated using the bottom level states, are considered in the top level where the subsystems are synchronized. The proposed methodology reduces system complexity while greatly improving tracking performance. The tracking error for each axis is considered in the bottom level where the Internal Model Principle is used to compensate for unmodeled nonlinear friction and slowly varying uncertainties. The top level goal (i.e., zero contour error) is propagated to the lower level by an aggregation relationship between contour error and physical linear axis variables. A controller is designed at the bottom level which simultaneously satisfies the bottom level goals (i.e., individual axis tracking) and the top level goal. Experimental results implemented on a table top CNC machine for diamond and freeform contours illustrate the performance of the proposed methodology. While this methodology was implemented for a two-axis motion system, it can be extended to any motion system containing more than two axes.     

Video object segmentation and tracking using region-based statistics

Two new region-based methods for video object tracking using active contours are presented. The first method is based on the assumption that the color histogram of the tracked object is nearly stationary from frame to frame. The proposed method is based on minimizing the color histogram difference between the estimated objects at a reference frame and the current frame using a dynamic programming framework. The second method is defined for scenes where there is an out-of-focus blur difference between the object of interest and the background. In such scenes, the proposed “defocus energy” can be utilized for automatic segmentation of the object boundary, and it can be combined with the histogram method to track the object more efficiently. Experiments demonstrate that the proposed methods are successful in difficult scenes with significant background clutter.     

Spatio-temporal object relationships in image sequences using adjacency matrices

In this work, we bring together object tracking and digital watermarking to solve the spatio-temporal object adjacency problem in image sequences. Spatio-temporal relationships are established by embedding objects with unique digital watermarks and then by propagating the watermark frame by frame. Watermark propagation is accomplished by an existing object tracking module so that a tracked object acquires its watermark from the correspondences established by the object tracker. The spatio-temporally marked image sequences can then be searched to establish spatial and temporal adjacency among objects without using traditional spatio-temporal graphs. Borrowing from graph theory, we construct binary adjacency matrices among tracked objects and develop interpretation rules to establish a track history for each object. Track history can be used to determine the arrival of new objects in frames or the changing of spatial and temporal positions of objects with respect to each other as they move through time and space.     

Automatic bootstrapping and tracking of object contours

A new fully automatic object tracking and segmentation framework is proposed. The framework consists of a motion-based bootstrapping algorithm concurrent to a shape-based active contour. The shape-based active contour uses finite shape memory that is automatically and continuously built from both the bootstrap process and the active-contour object tracker. A scheme is proposed to ensure that the finite shape memory is continuously updated but forgets unnecessary information. Two new ways of automatically extracting shape information from image data given a region of interest are also proposed. Results demonstrate that the bootstrapping stage provides important motion and shape information to the object tracker. This information is found to be essential for good (fully automatic) initialization of the active contour. Further results also demonstrate convergence properties of the content of the finite shape memory and similar object tracking performance in comparison with an object tracker with unlimited shape memory. Tests with an active contour using a fixed-shape prior also demonstrate superior performance for the proposed bootstrapped finite-shape-memory framework and similar performance when compared with a recently proposed active contour that uses an alternative online learning model.     

Orientation adaptive fast marching method for contour tracking of small intestine

The orientation of the small intestine varies among frames of a cinematic- magnetic resonance imaging (cine-MRI) sequence owing to small intestine peristalsis, which increases the complexity of its contour tracking. There are also leakage problems owing to the presence of low contrast areas in some frames. An improved fast marching method using an anisotropic Gaussian filter and a boundary penalty is presented for the contour tracking of small intestine segments. Experimental results show that the proposed method can track accurate contours of small intestine segments without leakage problems.     

基于主动轮廓模型联合Camshift算法的目标跟踪方法

针对城市道路交通环境中传统Camshift算法跟踪窗不能描述运动目标轮廓以及易受近似颜色干扰跟踪丢失的问题,提出基于Snake主动轮廓模型联合Chamshift区域模型的目标跟踪方法,算法将原Camshift算法中使用的HSV颜色特征和LTP纹理特征融合,增加了抗干扰能力,同时利用多尺度小波改进的Snake主动轮廓模型进一步对物体轮廓检测,去除阴影区域,更为直观的描述了物体的轮廓。实验证明,新方法在城市道路交通监控中有良好的应用前景。     

基于显著轮廓特征的SAR图像轮廓匹配新方法

在以星载SAR图像作为基准图、机载/弹载SAR图像作为实时图的匹配导航和精确制导研究中,传统基于点特征的匹配方法存在特征点数目过多, 误匹配率较高,容易受噪声及灰度变化影响等问题。该文提出一种基于显著轮廓特征的SAR图像“由粗到精”的匹配新方法。该方法在对SAR图像进行预处理的基础上,采用改进的模糊C均值聚类(FCM)的图像分割方法来提取闭合轮廓特征;采用归一化轮廓中心距离描述符进行双向匹配,获得强鲁棒性的粗匹配轮廓对;在粗匹配轮廓上采用改进的局部二值模式(LBP)算子得到精匹配结果。试验结果表明,该方法在图像旋转、空间变化以及噪声干扰较大的情况下,具有精确性高、鲁棒性强的优势,适宜遥感SAR图像匹配。     

Self-crossing detection and location for parametric active contours

Active contours are very popular tools for video tracking and image segmentation. Parameterized contours are used due to their fast evolution and have become the method of choice in the Sobolev context. Unfortunately, these contours are not easily adaptable to topological changes, and they may sometimes develop undesirable loops, resulting in erroneous results. To solve such topological problems, one needs an algorithm for contour self-crossing detection. We propose a simple methodology via simple techniques from differential topology. The detection is accomplished by inspecting the total net change of a given contour's angle, without point sorting and plane sweeping. We discuss the efficient implementation of the algorithm. We also provide algorithms for locating crossings by angle considerations and by plotting the four-connected lines between the discrete contour points. The proposed algorithms can be added to any parametric active-contour model. We show examples of successful tracking in real-world video sequences by Sobolev active contours and the proposed algorithms and provide ideas for further research.     

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.     

Real-time speaker tracking using particle filter sensor fusion

Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers are designed to generate effective proposals for the fuser. At the higher level, the fuser performs reliable tracking by verifying hypotheses over multiple likelihood models from multiple cues. Unlike traditional fusion algorithms, the proposed framework is a closed-loop system where the fuser and trackers coordinate their tracking information. Furthermore, to handle nonstationary situations, the proposed framework evaluates the performance of the individual trackers and dynamically updates their object states. We present a real-time speaker tracking system based on the proposed framework by fusing object contour, color and sound source location. We report robust tracking results.     

Level set analysis for leukocyte detection and tracking

We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.     

飞机噪声等值线绘制算法的研究

针对机场噪声的特征及分布特点,提出了一种基于路径栅格的机场噪声等值线追踪算法。通过构造有效网格、建立具有顶点和的路径栅格,在等值线追踪过程中唯一确定下一个等值点,能够快速生成等值线图,然后根据自身特点对等值线进行光滑,最后,为了方便查看不同区域的噪声影响情况,需要对等值线用不同的颜色进行填充处理。