Robust simultaneous detection of coronary borders in complex images

Visual estimation of coronary obstruction severity from angiograms suffers from poor inter- and intraobserver reproducibility and is often inaccurate. In spite of the widely recognized limitations of visual analysis, automated methods have not found widespread clinical use, in part because they too frequently fail to accurately identify vessel borders. The authors have developed a robust method for simultaneous detection of left and right coronary borders that is suitable for analysis of complex images with poor contrast, nearby or overlapping structures, or branching vessels. The reliability of the simultaneous border detection method and that of the authors' previously reported conventional border detection method were tested in 130 complex images, selected because conventional automated border detection might be expected to fail. Conventional analysis failed to yield acceptable borders in 65/130 or 50% of images. Simultaneous border detection was much more robust (p<.001) and failed in only 15/130 or 12% of complex images. Simultaneous border detection identified stenosis diameters that correlated significantly better with observer-derived stenosis diameters than did diameters obtained with conventional border detection (p<0.001), Simultaneous detection of left and right coronary borders is highly robust and has substantial promise for enhancing the utility of quantitative coronary angiography in the clinical setting.     

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     

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     

Segmentation of intravascular ultrasound images: a knowledge-based approach

Intravascular ultrasound imaging of coronary arteries provides important information about coronary lumen, wall, and plaque characteristics. Quantitative studies of coronary atherosclerosis using intravascular ultrasound and manual identification of wall and plaque borders are limited by the need for observers with substantial experience and the tedious nature of manual border detection. We have developed a method for segmentation of intravascular ultrasound images that identifies the internal and external elastic laminae and the plaque-lumen interface. The border detection algorithm was evaluated in a set of 38 intravascular ultrasound images acquired from fresh cadaveric hearts using a 30 MHz imaging catheter. To assess the performance of our border detection method we compared five quantitative measures of arterial anatomy derived from computer-detected borders with measures derived from borders manually defined by expert observers. Computer-detected and observer-defined lumen areas correlated very well (r=0.96, y=1.02x+0.52), as did plaque areas (r=0.95, y=1.07x-0.48), and percent area stenosis (r=0.93, y=0.99x-1.34.) Computer-derived segmental plaque thickness measurements were highly accurate. Our knowledge-based intravascular ultrasound segmentation method shows substantial promise for the quantitative analysis of in vivo intravascular ultrasound image data.     

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.     

Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging

Automated border detection using graph searching principles has been shown useful for many biomedical imaging applications. Unfortunately, in an often unpredictable subset of images, automated border detection methods may fail. Most current edge detection methods fail to take into account the added information available in a temporal or spatial sequence of images that are commonly available in biomedical image applications. To utilize this information the authors extended their previously reported single frame graph searching method to include data from a sequence. The authors' method transforms the three-dimensional surface definition problem in a sequence of images into a two-dimensional problem so that traditional graph searching algorithms may be used. Additionally, the authors developed a more efficient method of searching the three-dimensional data set using heuristic search techniques which vastly improve execution time by relaxing the optimality criteria. The authors have applied both methods to detect myocardial borders in computer simulated images as well as in short-axis magnetic resonance images of the human heart. Preliminary results show that the new multiple image methods may be more robust in certain circumstances when compared to a single frame method and that the heuristic search techniques may reduce analysis times without compromising robustness.     

舰船尾流红外图像边界检测方法

由于舰船尾流与周围未扰动的海水相比温差较小且无明显轮廓,导致舰船尾流红外图像成像模糊、对比度低,难以提取其边缘信息。文中根据舰船尾流及其红外图像的特点,提出了一种利用动态纹理和数学形态学检测尾流边界的方法。首先,利用动态纹理分割方法对舰船尾流红外图像序列进行了预分割。然后,采用Canny算子对尾流区进行边缘提取。对提取的边缘利用形态学的方法进行膨胀、标记和选择,提取了真正的尾流边界。实验结果表明:该方法对舰船尾流红外图像序列进行处理,能够有效的提取尾流的边界,取得了较好的处理效果。     

A motion control strategy based on equivalent mass matrix inmultidegree-of-freedom manipulator

This paper describes a decoupling motion control strategy based on an equivalent mass matrix in operational space. In the conventional approach, the equivalent mass matrix is defined as a function of Jacobian matrix and inertia of manipulator. Therefore, it is difficult to know the variation of the equivalent mass matrix precisely and to select it arbitrarily. This makes it difficult to realize the decoupling motion controller in the operational space. To improve the above problem, the authors introduce a robust control strategy based on a disturbance observer. In the observer-based approach, the equivalent mass matrix is determined arbitrarily independently of configuration and inertia variation of the manipulator. First, the equivalent mass matrix based on robust control is derived. Second, a simplification method of an operational space controller is discussed by using the equivalent mass matrix. Several experimental results are shown to confirm the validity of the proposed methods     

Ovarian ultrasound image analysis: follicle segmentation

Ovarian ultrasound is an effective tool in infertility treatment. Repeated measurements of the size and shape of follicles over several days are the primary means of evaluation by physicians. Currently, follicle wall segmentation is achieved by manual tracing which is time consuming and susceptible to inter-operator variation. An automated method for follicle wall segmentation is reported that uses a four-step process based on watershed segmentation and knowledge-based graph search algorithm which utilizes priori information about follicle structure for inner and outer wall detection. The automated technique was tested on 36 ultrasonographic images of women's ovaries. Validation against manually traced borders has shown good correlation of manually defined and computer-determined area measurements (R2 = 0.85 - 0.96). The border positioning errors were small: 0.63+/-0.36 mm for inner border and 0.67+/-0.41 mm for outer border detection. The use of watershed segmentation and graph search methods facilitates fast, accurate inner and outer border detection with minimal user-interaction.     

Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3-D roadmaps derived from preprocedural volumetric data can be used to add anatomical information. However, the registration between the 3-D roadmap and the 2-D X-ray image can be compromised by patient respiratory motion. Three methods were designed and evaluated to correct for respiratory motion using features in the 2-D X-ray images. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3-D model derived from 3-D preoperative volumetric data. The third method is based on tracking the coronary sinus (CS) catheter. This method uses blob detection to find all possible catheter electrodes in the X-ray image. A cost function is applied to select one CS catheter from all catheter-like objects. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2-D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking, 1.9 mm ± 1.0 mm for trachea tracking, and 1.8 mm ± 0.9 mm for CS catheter tracking. We present a comprehensive comparison between the techniques in terms of robustness, as computed by tracking errors, and accuracy, as computed by TRE using two independent approaches.     

基于区域定位与轮廓分割的红外目标检测

红外图像受随机噪声干扰严重.传统的基于高斯混合模型的检测算法检测得到的红外目标受虚假轮廓影响,不易准确辨识.为了准确识别红外目标,采用了一种基于脉冲耦合神经网络和高斯混合模型的红外目标检测算法.首先利用高斯混合模型定位红外目标区域的位置,然后利用基于空间信息的分水岭算法得到闭合区域,再利用基于脉冲耦合神经网络的分割算法剪切其虚影,最终检测到完整的运动目标.结果表明,该方法能够消除在传统方法中产生的虚影现象,得到精确的红外运动目标.通过比较,实验结果优于传统方法.     

Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images

Intravascular ultrasound (IVUS) provides direct depiction of coronary artery anatomy, including plaque and vessel area, which is important in quantitative studies on the progression or regression of coronary artery disease. Traditionally, these studies have relied on manual evaluation, which is laborious, time consuming, and subject to large interobserver and intraobserver variability. A new technique, called active surface segmentation, alleviates these limitations and makes strides toward routine analyses. However, for three-dimensional (3-D) plaque assessment or 3-D reconstruction to become a clinical reality, methods must be developed which can analyze many images quickly. Presented is a comparison between two active surface techniques for three-dimensional segmentation of luminal and medial-adventitial borders. The force-acceleration technique and the neighborhood-search technique accurately detected both borders in vivo (r2 = 0.95 and 0.99, Williams' index = 0.67 and 0.65, and r2 = 0.95 and 0.99, WI = 0.67 and 0.70, respectively). However, the neighborhood-search technique was significantly faster and required less computation. Volume calculations for both techniques (r2 = 0.99 and r2 = 0.99) also agreed with a known-volume phantom. Active surface segmentation allows 3-D assessment of coronary morphology and further developments with this technology will provide clinical analysis tools.     

Quantifying 3-D vascular structures in MRA images using hybrid PDE and geometric deformable models

The aim of this paper is to present a hybrid approach to accurate quantification of vascular structures from magnetic resonance angiography (MRA) images using level set methods and deformable geometric models constructed with 3-D Delaunay triangulation. Multiple scale filtering based on the analysis of local intensity structure using the Hessian matrix is used to effectively enhance vessel structures with various diameters. The level set method is then applied to automatically segment vessels enhanced by the filtering with a speed function derived from enhanced MRA images. Since the goal of this paper is to obtain highly accurate vessel borders, suitable for use in fluid flow simulations, in a subsequent step, the vessel surface determined by the level set method is triangulated using 3-D Delaunay triangulation and the resulting surface is used as a parametric deformable model. Energy minimization is then performed within a variational setting with a first-order internal energy; the external energy is derived from 3-D image gradients. Using the proposed method, vessels are accurately segmented from MRA data.     

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.     

Matched filter identification of left-ventricular endocardial borders in transesophageal echocardiograms

The use of one-dimensional spatial matched filtering for identifying the left ventricular endocardial borders in human transesophageal echocardiograms recorded during surgery is investigated. A maximum-likelihood method was used to choose the endocardial intensity profiles centered within the ventricle. The computer-generated border points were compared to those identified by an experienced ultrasonographer. A 16-pixel step template located 63.2% of the border points within 2 mm of the manual border. Median prefiltering of the images reduced detection accuracy by 3% to 6%. No statistically significant difference in accuracy was found between longer and shorter templates or between data-derived and step templates. Compared to manual estimates, computer generated cross-sectional area determinations were correlated with a coefficient of 0.93. Matched filtering executes rapidly, does not require prefiltering, and performs as well as other reported methods in estimating ventricular area.     

基于特征转移金字塔网络的SAR图像跨尺度目标检测

SAR图像多尺度目标检测能够实现大场景SAR图像中关键目标的定位与识别,是SAR图像解译的关键技术之一.然而针对尺寸相差较大的SAR目标的同时检测,即跨尺度目标检测问题,现有目标检测方法难以实现.该文提出一种基于特征转移金字塔网络(FTPN)的SAR图像跨尺度目标检测方法.在特征提取阶段采用特征转移方法,实现各层特征图...     

一种新的红外热像仪图像边缘检测方法

针对红外热像仪采集的红外影像边缘信息模糊、影像存在噪声、边缘信息难提取的特点,提出了一种基于数学形态学对LOG算子改进和Roberts算子数据相结合的边缘检测新方法。该方法首先引进形态学中的开闭运算对具有随机噪声的红外影像进行滤波,接着运用拉普拉斯算法边缘检测,然后再采用Roberts算子提取边缘信息,建立相应的融合规则及阈值条件,将两种方法检测出的影像边缘信息融合,得到最终的融合影像。最后,对增加椒盐噪声的影像用MATLAB进行仿真实验,结果表明,该方法结合了两种检测算子的优点,定位精度高,有很强的抗噪性,获得了比较理想的检测效果。     

Regions of interest extraction from SPECT images for neural degeneration assessment using multimodality image fusion

The aging population highlights the importance of early diagnosis of neurodegenerative diseases in the elderly. Current diagnoses of such diseases rely on visual assessment of the neuron activity of the specific regions in the brain revealed by SPECT imaging with a specific tracer, 99mTc-TRODAT-1. However, due to the difficulties in defining the regions of interest (ROI) in SPECT images, efficient indices are lacking for quantitative analysis. In this study, we performed simultaneous CT and SPECT scans and used the CT images as the medium to register the MR and SPECT images, such that the ROI delineated in the MR image can be mapped onto the SPECT image in the corresponding area. A robust registration scheme is proposed, including coarse registration using principal axes alignment and then fine-tuning the registration using a combination of maximal cross-section area detection and the general Hough transform. The results from three clinical datasets all show improved accuracy of registration as compared with the results obtained using conventional principal axes alignment alone. Based on these registration results, a correct ROI can be defined in the SPECT images and ROI-based quantitative indices can be further derived.     

The fuzzy Hough transform-feature extraction in medical images

Identification of anatomical features is a necessary step for medical image analysis. Automatic methods for feature identification using conventional pattern recognition techniques typically classify an object as a member of a predefined class of objects, but do not attempt to recover the exact or approximate shape of that object. For this reason, such techniques are usually not sufficient to identify the borders of organs when individual geometry varies in local detail, even though the general geometrical shape is similar. The authors present an algorithm that detects features in an image based on approximate geometrical models. The algorithm is based on the traditional and generalized Hough Transforms but includes notions from fuzzy set theory. The authors use the new algorithm to roughly estimate the actual locations of boundaries of an internal organ, and from this estimate, to determine a region of interest around the organ. Based on this rough estimate of the border location, and the derived region of interest, the authors find the final (improved) estimate of the true borders with other (subsequently used) image processing techniques. They present results that demonstrate that the algorithm was successfully used to estimate the approximate location of the chest wall in humans, and of the left ventricular contours of a dog heart obtained from cine-computed tomographic images. The authors use this fuzzy Hough transform algorithm as part of a larger procedure to automatically identify the myocardial contours of the heart. This algorithm may also allow for more rapid image processing and clinical decision making in other medical imaging applications.