Model-guided labeling of coronary structure

Assigning anatomic labels to coronary arteries in X-ray angiograms is an important task in medical imaging, motivated by the desire to standardize the assessment of coronary artery disease and to facilitate the three-dimensional (3-D) reconstruction and visualization of the coronary vasculature. However, automatic labeling poses a number of significant challenges, including the presence of noise, artifacts, competing structures, misleading visual cues, and other difficulties associated with a dynamic and inherently complex structure. The authors have developed a model-guided approach that addresses these challenges and automatically labels the vascular structure in coronary angiographic images. The approach consists of two models: (1) a symbolic model, represented through a directed acyclic graph, that captures vascular tree hierarchies and branch interrelationships and (2) a generalized 3-D model that captures spatial and geometric relationships. Importantly, the approach detects ambiguities (such as vessel overlaps) that may be found in a frame of a cine sequence, and resolves these ambiguities by considering the information derived from other (unambiguous) frames in the temporal sequence, employing dynamic programming methods to match the image features found in the different (ambiguous and unambiguous) frames. This paper presents this model-guided labeling algorithm and discusses the experimental results obtained from implementing and applying the resulting labeling system to a variety of clinical images. The results indicate the feasibility of achieving robust and consistently accurate image labeling through this model-guided, temporal disambiguation method     

Lumen centerline detection in complex coronary angiograms

The authors have developed a method for lumen centerline detection in individual coronary segments that is based on simultaneous detection of the approximate positions of the left and right coronary borders. This approach emulates that of a clinician who visually identifies the lumen centerline as the midline between the simultaneously-determined left and right borders of the vessel segment of interest. The authors' lumen centerline detection algorithm and 2 conventional centerline detection methods were compared to carefully-defined observer-identified centerlines in 89 complex coronary images. Computer-detected and observer-defined centerlines were objectively compared using 5 indices of center line position and orientation. The quality of centerlines obtained with the new simultaneous border identification approach and the 2 conventional centerline detection methods was also subjectively assessed by an experienced cardiologist who was unaware of the analysis method. The authors' centerline detection method yielded accurate centerlines in the 89 complex images. Moreover, their method outperformed the 2 conventional methods as judged by all 5 objective parameters (p<0.001 for each parameter) and by the subjective assessment of centerline quality (p<0.001). Automated detection of lumen centerlines based on simultaneous detection of both coronary borders provides improved accuracy in complex coronary arteriograms     

Automated analysis of coronary arterial morphology in cineangiograms: geometric and physiologic validation in humans

A method of coronary border identification is discussed that is based on graph searching principles and is applicable to the broad spectrum of angiographic image quality observed clinically. Cine frames from clinical coronary angiograms were optically magnified, digitized, and graded for image quality. Minimal lumen diameters, referenced to catheter size, were derived from automatically identified coronary borders and compared to those defined using quantitative coronary arteriography and to observer-traced borders. computer-derived minimal lumen diameters were also compared to intracoronary measurements of coronary vasodilator reserve, a measure of the functional significance of a coronary obstruction. To test the robustness of the present border detection method, computer-derived coronary borders were compared to independent standards separately for good and poor angiographic images. The accuracy of computer-identified borders was similar in the two cases.     

Bidirectional labeling and registration scheme for grayscale image segmentation.

In this paper, we introduce a new image segmentation scheme that is based on bidirectional labeling and registration and prove that its segmentation performance is equivalent to that of the conventional watershed segmentation algorithm. The proposed bidirectional labeling and registration scheme, which we refer to as bidirectional labeling and registration scheme (BIDS), involves only linear scans of image pixels. It uses one-dimensional operations rather than the queues that are used in traditional segmentation algorithms, which are two-dimensional problems. BIDS also provides unique labels for individual homogeneous regions. In addition to achieving the same segmentation results, BIDS is four times less computationally complex than the conventional watershed by immersion technique.     

Estimating the 3D skeletons and transverse areas of coronary arteries from biplane angiograms

A method for estimating the three-dimensional (3D) skeletons and transverse areas of the lumens of coronary arteries from digital X-ray angiograms is described. The method is based on the use of a 3D generalized cylinder (GC) consisting of a series of 3D elliptical disks transverse to and centered on a 3D skeleton (medial axis) of the coronary arteries. The estimates of the transverse areas are based on a nonlinear least-squares-error estimation technique described by D.W. Marquardt (1963). This method exploits densitometric profiles, boundary estimates, and the orientation of the arterial skeleton in 3-space and includes an automatic artery tracking procedure. It applies an adaptive window to the densitometric profile data that are used in the parameter estimation. Preliminary experimental tests of the procedure on angiograms of in vivo human coronaries and on synthetic images yield encouraging results.     

Three-dimensional motion tracking of coronary arteries in biplane cineangiograms

A three-dimensional (3-D) method for tracking the coronary arteries through a temporal sequence of biplane X-ray angiography images is presented. A 3-D centerline model of the coronary vasculature is reconstructed from a biplane image pair at one time frame, and its motion is tracked using a coarse-to-fine hierarchy of motion models. Three-dimensional constraints on the length of the arteries and on the spatial regularity of the motion field are used to overcome limitations of classical two-dimensional vessel tracking methods, such as tracking vessels through projective occlusions. This algorithm was clinically validated in five patients by tracking the motion of the left coronary tree over one cardiac cycle. The root mean square reprojection errors were found to be submillimeter in 93% (54/58) of the image pairs. The performance of the tracking algorithm was quantified in three dimensions using a deforming vascular phantom. RMS 3-D distance errors were computed between centerline models tracked in the X-ray images and gold-standard centerline models of the phantom generated from a gated 3-D magnetic resonance image acquisition. The mean error was 0.69 (+/- 0.06) mm over eight temporal phases and four different biplane orientations.     

基于分水岭分割和尺度不变特征点的多目标全自主跟踪算法

该文针对多目标的鲁棒跟踪问题,设计了一种基于图像分水岭分割和尺度不变特征变换(SIFT)特征点的多目标全自主跟踪算法。为规避图像平坦区域,提出在原图上叠加规则坡度图的思想,并在浮点域进行一定尺度高斯模糊处理,将区域极小值点作为种子点完成分水岭分割,并将极值点作为目标特征点,通过前后帧分水岭映射生成特征点短时轨迹,自动检测运动目标。之后依据目标所处状态(是否发生遮挡)和分水岭分割图建立、更新目标SIFT特征池,结合分水岭映射、SIFT特征池匹配完成对目标的鲁棒跟踪。实验结果表明,该算法能有效完成视频中多目标的持续跟踪,并对目标遮挡有较好的鲁棒性。     

运动冠脉造影序列的独立分量分析伪彩色编码

针对心脏周期剧烈运动产生的冠状动脉血管非刚性变化所引起的造影图像处理误差,提出一种基于快速独立分量分析(FastICA)的动态冠脉造影图像序列特征提取方法,改进了以往算法将动态序列中的每帧作为静态图像单独处理而未考虑帧间关系的缺点,综合序列中各帧信息进行分析,解混分离出背景区和感兴趣区。在传统FastICA算法基础上,提出一种适合冠脉造影图像灰度级分布特征的自适应初始权值计算方法,在保证效果一致的情况下,使迭代次数降低约45%。最后,设计一种基于感兴趣区域的伪彩色编码方法。     

Adaptive approach to accurate analysis of small-diameter vessels incineangiograms

In coronary vessels smaller than 1 mm in diameter, it is difficult to accurately identify lumen borders using existing border detection techniques. Computer-detected diameters of small coronary vessels are often severely overestimated due to the influence of the imaging system point spread function and the use of an edge operator designed for a broad range of diameter vessel sizes. Computer-detected diameters may be corrected if a calibration curve for the X-ray system is available. Unfortunately, the performance of this postprocessing diameter correction approach is severely limited by the presence of image noise. The authors report here a new approach that uses a two-stage adaption of edge operator parameters to optimally match the edge operator to the local lumen diameter. In the first stage, approximate lumen diameters are detected using a single edge operator in a half-resolution image. Depending on the approximate lumen size, one of three edge operators is selected for the second full-resolution stage in which left and right coronary borders are simultaneously identified. The method was tested in a set of 72 segments of nine angiographic phantom vessels with diameters ranging from 0.46 to 4.14 mm and in 82 clinical coronary angiograms. Performance of the adaptive simultaneous border detection method was compared to that of a conventional border detection method and to that of a postprocessing diameter correction border detection method. Adaptive border detection yielded significantly improved accuracy in small phantom vessels and across all vessel sizes in comparison to the conventional and postprocessing diameter correction methods (p<0.001 in all cases). Adaptive simultaneous coronary border detection provides both accurate and robust quantitative analysis of coronary vessels of all sizes     

Simultaneous detection of both coronary borders

A method for simultaneous detection of both coronary borders that is based on three-dimensional graph searching principles is presented. The simultaneous method and the authors' previously reported conventional method were applied to 29 coronary images, of which 19 were selected because conventional methods might be expected to have difficulty. Coronary borders identified by the two methods were visually compared. In the 19 difficult images, simultaneous border detection yielded superior results in 7 images and equivalent results in 12 images. Superior or equivalent results were obtained in the remaining 10 typical images. In a set of 43 uncomplicated images, minimal lumen diameters derived using simultaneous border detection correlated well with diameters derived using conventional border detection (r=0.97), diameters obtained from observer-defined borders (r=0.91), and diameters obtained using the Brown-Dodge quantitative coronary arteriography method (r=0.85). Thus simultaneous detection of left and right coronary borders provides improved accuracy in the detection of vessel borders in difficult coronary angiograms.     

An Embedded Real-Time Surveillance System: Implementation and Evaluation

This paper presents the design of an embedded automated digital video surveillance system with real-time performance. Hardware accelerators for video segmentation, morphological operations, labeling and feature extraction are required to achieve the real-time performance while tracking will be handled in software in an embedded processor. By implementing a complete embedded system, bottlenecks in computational complexity and memory requirements can be identified and addressed. Accordingly, a memory reduction scheme for the video segmentation unit, reducing bandwidth with more than 70%, and a low complexity morphology architecture that only requires memory proportional to the input image width, have been developed. On a system level, it is shown that a labeling unit based on a contour tracing technique does not require unique labels, resulting in more than 50% memory reduction. The hardware accelerators provide the tracking software with image objects properties, i.e. features, thereby decoupling the tracking algorithm from the image stream. A prototype of the embedded system is running in real-time, 25 fps, on a field programmable gate array development board. Furthermore, the system scalability for higher image resolution is evaluated.     

基于T-snake模型的冠状动脉血管提取和运动跟踪

提出采用拓扑自适应动态轮廓(T-snake)模型对X射线冠状动脉造影图像序列进行二维血管提取和运动跟踪的方法,得到心动周期中冠状动脉的二维形态和运动信息.设计了适用于心血管造影图像的强约束T-snake模型,约束节点沿网格线从一个网格点运动到下一个网格点,通过节点拆分获得拓扑变换能力.针对图像灰度统计特征,设计了使模型能从血管内部的初始位置膨胀变形的能量函数.采用临床采集的X射线冠状动脉造影图像序列对算法进行了验证.     

Assessment of diffuse coronary artery disease by quantitative analysis of coronary morphology based upon 3-D reconstruction from biplane angiograms

Quantitative evaluations on coronary vessel systems are of increasing importance in cardiovascular diagnosis, therapy planning, and surgical verification. Whereas local evaluations, such as stenosis analysis, are already available with sufficient accuracy, global evaluations of vessel segments or vessel subsystems are not yet common. Especially for the diagnosis of diffuse coronary artery diseases, the authors combined a 3D reconstruction system operating on biplane angiograms with a length/volume calculation. The 3D reconstruction results in a 3D model of the coronary vessel system, consisting of the vessel skeleton and a discrete number of contours. To obtain an utmost accurate model, the authors focussed on exact geometry determination. Several algorithms for calculating missing geometric parameters and correcting remaining geometry errors were implemented and verified. The length/volume evaluation can be performed either on single vessel segments, on a set of segments, or on subtrees. A volume model based on generalized elliptical conic sections is created for the selected segments. Volumes and lengths (measured along the vessel course) of those elements are summed up. In this way, the morphological parameters of a vessel subsystem can be set in relation to the parameters of the proximal segment supplying it. These relations allow objective assessments of diffuse coronary artery diseases.     

基于SIFT的边缘局部仿射的图像拼接算法

本文针对累计误差形成的多幅图像拼接产生畸变的问题,提出了一种基于边缘局部仿射的拼接算法,在拼接过程中逐步衰减仿射变换,使得图像在拼接过程中逐渐降低过仿射带来的畸变.同时为从视觉上分析图像拼接过程特征点的变化情况,本文还提出了一种基于特征点的骨架图算法,通过拼接后骨架图的图像来分析各种拼接算法.可以广泛应用于拼接算法的测试过程.     

Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm

A tracking algorithm for identification of vessel contours in digital coronary arteriograms was developed and validated. Given an initial start-of-search point, the tracking process was fully automated by utilizing the spatial continuity of the vessel's centerline, orientation, diameter, and density. The incremental sections along a major vessel were sequentially identified, based on the assumptions of geometric similarity and continuation between adjacent incremental sections. The algorithm consisted of an extrapolation-update process which was guided by a matched filter. The filter parameters were adapted to the measured lumen width. The tracking process was robust and extremely efficient as indicated by test results on synthetic images, digital subtraction angiograms, and cineangiograms. The algorithm provided accurate measurement of lumen width and percent stenosis that was relatively invariant to the vessel's orientation, dynamic range, background variation, and degree of blurring     

Accuracy assessment of layer decomposition using simulatedangiographic image sequences

Layer decomposition is a promising method for obtaining accurate densitometric profiles of diseased coronary artery segments. This method decomposes coronary angiographic image sequences into moving densitometric layers undergoing translation, rotation, and scaling. In order to evaluate the accuracy of this technique, we have developed a technique for embedding realistic simulated moving stenotic arteries in real clinical coronary angiograms. We evaluate the accuracy of layer decomposition in two ways. First, we compute tracking errors as the distance between the true and estimated motion of a reference point in the arterial lesion. We find that noise-weighted phase correlation and layered background subtraction are superior to cross correlation and fixed mask subtraction, respectively. Second, we compute the correlation coefficient between the true vessel profile and the raw and processed images in the region of the stenosis. We find that layer decomposition significantly improves the correlation coefficient.     

Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme

A computer algorithm was developed for automated identification of 2-D vascular networks in X-ray angiograms. This was accomplished by using an adaptive tracking algorithm in a three-stage recursive procedure. First, given a starting position and direction, a segment in the vascular network was identified. Second, by filling it with the surrounding background pixel values, the detected segment was deleted from the angiogram. The detection-deletion scheme was employed to prevent the problem of tracking-path reentry in those areas where vessels overlap. Third, all branch points were detected by use of matched filtering along both edges of the vessel. The detected branch points were used as the starting points in the next recursion. The recursive procedure terminated when no new branch point was found. The algorithm showed a good performance when it was applied to angiograms of coronary and radial arteries. To provide a quantitative evaluation, vascular networks identified by the algorithm were compared to those identified by a human. The algorithm made some false-negative errors, but very few false-positive errors.     

Multimedia annotation via semi-supervised shared-subspace feature selection

With the rapid development of social network and computer technologies, we always confront with high-dimensional multimedia data. It is time-consuming and unrealistic to organize such a large amount of data. Most existing methods are not appropriate for large-scale data due to their dependence of Laplacian matrix on training data. Normally, a given multimedia sample is usually associated with multiple labels, which are inherently correlated to each other. Although traditional methods could solve this problem by translating it into several single-label problems, they ignore the correlation among different labels. In this paper, we propose a novel semi-supervised feature selection method and apply it to the multimedia annotation. Both labeled and unlabeled samples are sufficiently utilized without the need of graph construction, and the shared information between multiple labels is simultaneously uncovered. We apply the proposed algorithm to both web page and image annotation. Experimental results demonstrate the effectiveness of our method.     

A new model-based technique for enhanced small-vessel measurements in X-ray ciné-angiograms

Arterial diameter estimation from X-ray ciné angiograms is important for quantifying coronary artery disease (CAD) and for evaluating therapy. However, diameter measurement in vessel cross sections < or =1.0 mm is associated with large measurement errors. We present a novel diameter estimator which reduces both magnitude and variability of measurement error. We use a parametric nonlinear imaging model for X-ray ciné angiography and estimate unknown model parameters directly from the image data. Our technique allows us to exploit additional diameter information contained within the intensity profile amplitude, a feature which is overlooked by existing methods. This method uses a two-step procedure: the first step estimates the imaging model parameters directly from the angiographic frame and the second step uses these measurements to estimate the diameter of vessels in the same image. In Monte-Carlo simulation over a range of imaging conditions, our approach consistently produced lower estimation error and variability than conventional methods. With actual X-ray images, our estimator is also better than existing methods for the diameters examined (0.4-4.0 mm). These improvements are most significant in the range of narrow vessel widths associated with severe coronary artery disease.