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.     

Semiautomatic detection of scoliotic rib borders from posteroanterior chest radiographs

3-D assessment of scoliotic deformities relies on an accurate 3-D reconstruction of bone structures from biplanar X-rays, which requires a precise detection and matching of anatomical structures in both views. In this paper, we propose a novel semiautomated technique for detecting complete scoliotic rib borders from PA-0° and PA-20° chest radiographs, by using an edge-following approach with multiple-path branching and oriented filtering. Edge-following processes are initiated from user starting points along upper and lower rib edges and the final rib border is obtained by finding the most parallel pair among detected edges. The method is based on a perceptual analysis leading to the assumption that no matter how bent a scoliotic rib is, it will always present relatively parallel upper and lower edges. The proposed method was tested on 44 chest radiographs of scoliotic patients and was validated by comparing pixels from all detected rib borders against their reference locations taken from the associated manually delineated rib borders. The overall 2-D detection accuracy was 2.64 ± 1.21 pixels. Comparing this accuracy level to reported results in the literature shows that the proposed method is very well suited for precisely detecting borders of scoliotic ribs from PA-0° and PA-20° chest radiographs.     

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.     

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.     

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.     

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     

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.     

Locally linear regression for pose-invariant face recognition.

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.     

Automatic detection of rib borders in chest radiographs

An algorithm for detection of posterior rib borders in chest radiographs is presented. The algorithm first determines the thoracic cage boundary to restrict the area of search for the ribs. It then finds approximate rib borders using a knowledge-based Hough transform. Finally, the algorithm localizes the rib borders using an active contour model. Results of the proposed rib finding algorithm on 10 chest radiographs are presented.     

Enhancement of mammographic features by optimal adaptive neighborhood image processing

X-ray mammography is the only breast cancer detection technique presently available with proven efficacy. Mammographic detection of early breast cancer requires optimal radiological or image processing techniques. We present an image processing approach based on adaptive neighborhood processing with a new set of contrast enhancement functions to enhance mammographic features. This procedure brings out the features in the image with little or no enhancement of the noise. We also find that adaptive neighborhoods with surrounds whose width is a constant difference from the center yield improved enhancement over adaptive neighborhoods with a constant ratio of surround to center neighborhood widths.     

飞行人员鼻窦囊肿的诊治和医学鉴定

目的探讨飞行人员鼻窦囊肿（CNS）的发生机制、临床诊治和医学鉴定原则。方法总结飞行人员CNS资料46份,对其进行分类：结果黏膜囊肿44例,黏液囊肿和含牙囊肿各1例。上颌窦囊肿39例,蝶窦囊肿3例,筛窦囊肿、额窦囊肿、上颌窦＋额窦囊肿、上颌窦＋蝶窦囊肿各1例。Ⅰ类（地面有症状或体检时发现）37例,Ⅱ类（地面无症状,飞行中有继发性气压伤表现）9例。手术治疗16例,观察30例：飞行合格41例,永久停飞5例。结论飞行中气压变化的反复刺激可能是飞行人员CNS的发生机制之一,Ⅱ类CNS是医学停飞的重要原因。     

Automatic Surface Matching for the Registration of LIDAR Data and MR Imagery

Several photogrammetric and geographic information system applications such as surface matching, object recognition, city modeling, environmental monitoring, and change detection deal with multiple versions of the same surface that have been derived from different sources and/or at different times. Surface registration is a necessary procedure prior to the manipulation of these 3D datasets. This need is also applicable in the field of medical imaging, where imaging modalities such as magnetic resonance imaging (MRI) can provide temporal 3D imagery for monitoring disease progression. This paper will present a general automated surface registration procedure that can establish correspondences between conjugate surface elements. Experimental results using light detection and ranging (LIDAR) and MRI data will verify the feasibility, robustness, and accuracy of this approach.     

面向ATR的平面隙缝阵列天线电磁散射特性研究

该文针对主动雷达导引头对防空导弹武器系统制导雷达的检测与识别需求,通过对制导雷达平面隙缝阵列天线电磁散射机理的分析,建立了天线的电磁散射模型,研究了天线RCS的计算方法,结合仿真计算结果,给出了天线尺寸、隙缝数目及入射波频率等因素对天线RCS的影响规律。仿真结果表明,雷达天线RCS较大且具有明显的周期性,其幅度特征和周期特征可以作为识别的重要依据,为主动雷达导引头对地面雷达站的检测与识别奠定了基础。     

新的红外图像小目标检测方法

在分析了海空背景下舰船目标红外图像特征的基础上．提出了一种基于背景粗糙度估计的红外图像小目标自适应检测算法,该算法利用LOG算子检测目标的大致轮廓,确定目标的中心点作为区域生长的种子点,可以看作是为后续处理压缩数据量的一种方法。它可以减少运算量,提高检测速度,抑制对不必要的种子点的区域生长,提高目标的检测概率。     

A novel method for detection of the transition between atrial fibrillation and sinus rhythm

Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.     

Automatic detection and recognition of signs from natural scenes

In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.     

Time-dependent signatures of acoustic wave biosensors

Acoustic wave devices coated with a biolayer represent one biosensor approach for the detection of medically relevant biomolecules. In a typical application, the acoustic wave device is connected in an oscillator circuit, and the frequency shift /spl Delta/f resulting from a biomolecular event is recorded. In this paper, we discuss our recent work in this field, which has included the use of Rayleigh wave surface acoustic wave devices for vapor phase detection as well as quartz crystal microbalance devices for liquid phase measurements. For all of the results reported herein the biofilm on the surface of the acoustic wave device consists of a layer of antibodies raised against a specific target molecule or antigen. We present our results for the vapor phase detection of small molecules such as uranine and cocaine as well as liquid phase detection of small and large molecules. The data we present from these various experiments is the signature associated with the biomolecular recognition events; that is, we record and present /spl Delta/f(t). Finally, we present the recent results of our time-dependent perturbation theory work, which gives a potential method for resolving the acoustic wave biosensor signature into information relating to molecular structure changes during a molecular recognition event.