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.     

A multiple active contour model for cardiac boundary detection on echocardiographic sequences

Tracing of left-ventricular epicardial and endocardial borders on echocardiographic sequences is essential for quantification of cardiac function. The authors designed a method based on an extension of active contour models to detect both epicardial and endocardial borders on short-axis cardiac sequences spanning the entire cardiac cycle. They validated the results by comparing the computer-generated boundaries to the boundaries manually outlined by four expert observers on 44 clinical data sets. The mean boundary distance between the computer-generated boundaries and the manually outlined boundaries was 2.80 mm (sigma=1.28 mm) for the epicardium and 3.61 (sigma=1.68 mm) for the endocardium. These distances were comparable to interobserver distances, which had a mean of 3.79 mm (sigma=1.53 mm) for epicardial borders and 2.67 mm (sigma=0.88 mm) for endocardial borders. The correlation coefficient between the areas enclosed by the computer-generated boundaries and the average manually outlined boundaries was 0.95 for epicardium and 0.91 for endocardium. The algorithm is fairly insensitive to the choice of the initial curve. Thus, the authors have developed an effective and robust algorithm to extract left-ventricular boundaries from echocardiographic sequences.     

Knowledge-based image analysis for automated boundary extraction of transesophageal echocardiographic left-ventricular images

A system for automatically determining the contour of the left ventricle (LV) and its bounded area, from transesophageal echocardiographic (TEE) images is presented. It uses knowledge of both heart anatomy and echocardiographic imaging to guide the selection of image processing methodologies for thresholding, edge detection, and contour following and the center-based boundary-finding technique to extract the contour of the LV region. To speed up the processing a rectangular region of interest from a TEE picture is first isolated and then reduced to a coarse version, one-ninth original size. All processing steps, except the final contour edge extraction, are performed on this reduced image. New methods developed for automatic threshold selection, region segmentation, noise removal, and region center determination are described.     

A comparative analysis of several transformations for enhancement and segmentation of magnetic resonance image scene sequences

The performance of the eigenimage filter is compared with those of several other filters as applied to magnetic resonance image (MRI) scene sequences for image enhancement and segmentation. Comparisons are made with principal component analysis, matched, modified-matched, maximum contrast, target point, ratio, log-ratio, and angle image filters. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), segmentation of a desired feature (SDF), and correction for partial volume averaging effects (CPV) are used as performance measures. For comparison, analytical expressions for SNRs and CNRs of filtered images are derived, and CPV by a linear filter is studied. Properties of filters are illustrated through their applications to simulated and acquired MRI sequences of a phantom study and a clinical case; advantages and weaknesses are discussed. The conclusion is that the eigenimage filter is the optimal linear filter that achieves SDF and CPV simultaneously.     

在线磁共振图像重建研究

实现了一种磁共振成像(MRI)系统的在线图像重建方案:研发数据仿真客户端和图像重建服务器端程序,模拟了实时在线磁共振图像重建;利用西门子MRI系统的ICE框架开发了数据通信客户端程序,并基于YAP框架开发了数据重建服务器端程序,实现了外部计算机接入MRI系统流水线的实时在线重建。这种重建方法有利于MRI算法的集成与应用。     

红外图像序列的目标增强和检测

文中主要研究了远距离红外图像序列的目标增强和检测问题,提出了基于局部纹理特征的红外图像增强算法,利用红外图像目标和背景区域局部纹理特征的差异来增强目标和背景区域的对比度;为了提高目标检测的速度和精度,利用序列图像的帧间相关信息,采用边检测边跟踪边确认的目标检测方法。实验结果表明,对于远距离、小目标的红外图像,这种增强算法在目标对比度和细节方面都明显优于直方图均衡化方法,而本文采用的目标检测方法仅需很少几帧图像就能检测和跟踪目标,可以在确保目标检测的可靠性的前提下,提高目标的检测速度。     

基于红外图像的地面自主车导航的道路边界检测

讨论了用于地面自主车（ALV）视觉导航的红外道路图像的特点,论述了红外道路图像的预处理策略和方法,分析了一些常用的图像处理方法在处理红外道路图像时的缺陷,指出由于温差的渐变性。红外道路图像的路边检测特别适合使用阈值穷举法．并给出了在温度渐变的路边上边缘强度的求取方法和使用二阶微分算子得到边界线段的精确位置的方法。同时使用了边缘信息和灰度信息来提取道路边界信息,阈值穷举法和二阶微分算子得到的道路边界线段相互融合。得到候选的道路边界线段,在ALV的实际应用中取得了很好的效果。     

Geometric attraction-driven flow for image segmentation and boundary detection

Novel forces in image segmentation based on active contours models are proposed for capturing objects in the image. Contemplating the common functionality of forces in previous active contours models, we propose the geometric attraction-driven flow (GADF), the binary edge function, and the binary balloon forces to detect objects in difficult cases such as varying illumination and complex shapes. The orientation of GADF is orthogonally aligned with the boundary of object and has the opposite direction across the boundary. It prevents the leakage through weak edges of objects, which occur due to illumination. To reduce the interference from other forces, we design the binary edge function using the property of the orientation in the GADF. We also design the binary balloon force based on the four-color theorem. Combining with initial dual level set functions, the proposed model captures holes in objects and multiple junctions from different colors. The result does not depend on positions of initial contours.     

Tomographic reconstruction of dynamic cardiac image sequences.

In this paper, we propose an approach for the reconstruction of dynamic images from a gated cardiac data acquisition. The goal is to obtain an image sequence that can show simultaneously both cardiac motion and time-varying image activities. To account for the cardiac motion, the cardiac cycle is divided into a number of gate intervals, and a time-varying image function is reconstructed for each gate. In addition, to cope with the under-determined nature of the problem, the time evolution at each pixel is modeled by a B-spline function. The dynamic images for the different gates are then jointly determined using maximum a posteriori estimation, in which a motion-compensated smoothing prior is introduced to exploit the similarity among the different gates. The proposed algorithm is evaluated using a dynamic version of the 4-D gated mathematical cardiac torso phantom simulating a gated single photon emission computed tomography perfusion acquisition with Technitium-99m labeled Teboroxime. We thoroughly evaluated the performance of the proposed algorithm using several quantitative measures, including signal-to-noise ratio analysis, bias-variance plot, and time activity curves. Our results demonstrate that the proposed joint reconstruction approach can improve significantly the accuracy of the reconstruction.     

EdgeFlow: a technique for boundary detection and image segmentation

A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. Segmentation results on a large and diverse collections of natural images are provided, demonstrating the usefulness of this method to content based image retrieval.     

Permutation testing made practical for functional magnetic resonance image analysis

We describe an efficient algorithm for the step-down permutation test, applied to the analysis of functional magnetic resonance images. The algorithm's time bound is nearly linear, making it feasible as an interactive tool. Results of the permutation test algorithm applied to data from a cognitive activation paradigm are compared with those of a standard parametric test corrected for multiple comparisons. The permutation test identifies more weakly activated voxels than the parametric test, always activates a superset of the voxels activated by this parametric method, almost always yields significance levels greater than or equal to those produced by the parametric method, and tends to enlarge activated clusters rather than adding isolated voxels. Our implementation of the permutation test is freely available as part of a widely distributed software package for analysis of functional brain images.     

Homodyne detection in magnetic resonance imaging

Magnetic detection of complex images in magnetic resonance imaging (MRI) is immune to the effects of incidental phase variations, although in some applications information is lost or images are degraded. It is suggested that synchronous detection or demodulation can be used in MRI systems in place of magnitude detection to provide complete suppression of undesired quadrature components, to preserve polarity and phase information, and to eliminate the biases and reduction in signal-to-noise ratio (SNR) and contrast in low SNR images. The incidental phase variations in an image are removed through the use of a homodyne demodulation reference, which is derived from the image or the object itself. Synchronous homodyne detection has been applied to the detection of low SNR images, the reconstruction of partial k-space images, the simultaneous detection of water and lipid signals in quadrature, and the preservation of polarity in inversion-recovery images.     

Object detection using spatio-temporal thresholding in image sequences

An algorithm using spatio-temporal thresholding for object detection with spatio-temporal distance metric in image sequences is proposed. The distance metric consists of the feature which uses the intensity and gradient at the same time in feature level instead of in decision level. In the model update process truncated variable adaptation rate is used, which can control adaptation rate up to its statistics, so it is able to maintain its statistics properly through the whole sequence. Some experimental results in various environments show that the averaged performance of the proposed algorithm is good.     

Object detection using multi-resolution mosaic in image sequences

Object detection in image sequences has a very important role in many applications such as surveillance systems, tracking and recognition systems, coding systems and so on. This paper proposes a unified framework for background subtraction, which is very popular algorithm for object detection in image sequences. And we propose an algorithm using spatio-temporal thresholding and truncated variable adaptation rate (TVAR) for object detection and background adaptation, respectively. Especially when the camera moves and zooms in on something to track the target, we generate multi-resolution mosaic which is made up of many background mosaics with different resolution, and use it for object detection. Some experimental results in various environments show that the averaged performance of the proposed algorithm is good.     

A fast encryption algorithm of color image based on four-dimensional chaotic system

As the low complexity of low-dimensional chaotic system and the slow speed of image encryption, this paper proposes a fast encryption algorithm of color image based on four-dimensional chaotic system. Firstly, we propose a new method of designing four-dimensional chaotic system based on the classical equations of three-dimensional chaotic system, to increase the complexity and key space of the encryption algorithm. Secondly, according to the nature of color images’ pixels channel, we design a new pseudo-random sequence generator and reuse the random sequence, to improve the speed of image encryption. Finally, the methods of row-major and column-major are used to diffuse the original image and the cat map with parameter is used to scramble the image pixels, respectively, to achieve the effect of encryption. The results of simulation and security analysis show that the proposed encryption algorithm is of good performance on security, robustness and high encryption speed.     

基于RBM-KNN的脑部磁共振图像分类

为加快医学图像分类速度,提高分类精确率,文中采用受限玻尔兹曼机(Restricted Boltzmann Machine,RBM)结合K近邻(K-Nearest Neighbor,KNN)分类器方法。首先构建可视层二值对隐层二值RBM,利用RBM训练得到特征提取器,该特征提取器可同时实现特征降维,然后特征提取器从像素单元直接提取图像特征,最后用KNN将特征分类,并用测试样本检验分类准确性。将文中方法用在脑部磁共振图像数据库分类中,实验结果表明,提出的方法具有良好的分类准确率,且明显高于基于单一统计特征提取的医学图像分类方法。     

红外序列图像弱小目标检测算法

红外图像中的弱小目标因为信噪比和对比度较低,用单帧图像检测很难得到满意的效果,利用多帧图像信息进行检测是有效的手段之一。分析了利用帧间相关性对红外图像中的弱小目标进行检测的方法,提出了改进的算法和对比度相关性的概念。该算法在序列图像灰度起伏情况下,克服了直接利用灰度相关效果不好的缺点,具有良好的抗噪声和背景干扰的特点。算法可以推广到其他类型图像目标的检测中。     

一种序列图像中运动点目标的检测方法

序列图像中运动点目标的检测按图像的成像系统不同,可分为红外图像中运动点目标的检测和可见光图像中运动点目标的检测,而现有检测算法多是针对前者。为寻找一种适用于两种类型图像的运动点目标的检测方法,以多云天空为研究背景,提出了一种新的运动点目标检测算法。采用高通滤波和形态学滤波相结合的方法进行背景抑制,基于检测前跟踪（TBD）的基本思想,根据相邻三帧进行目标分割,利用轨迹能量累积方法完成目标检测。理论分析和仿真结果表明,该算法简单易行．既适用于红外图像又适用于可见光图像的运动点目标,而且对目标的运动速度和方向无任何限制。     

Semantic image retrieval in magnetic resonance brain volumes

Practitioners in the area of neurology often need to retrieve multimodal magnetic resonance (MR) images of the brain to study disease progression and to correlate observations across multiple subjects. In this paper, a novel technique for retrieving 2-D MR images (slices) in 3-D brain volumes is proposed. Given a 2-D MR query slice, the technique identifies the 3-D volume among multiple subjects in the database, associates the query slice with a specific region of the brain, and retrieves the matching slice within this region in the identified volumes. The proposed technique is capable of retrieving an image in multimodal and noisy scenarios. In this study, support vector machines (SVM) are used for identifying 3-D MR volume and for performing semantic classification of the human brain into various semantic regions. In order to achieve reliable image retrieval performance in the presence of misalignments, an image registration-based retrieval framework is developed. The proposed retrieval technique is tested on various modalities. The test results reveal superior robustness performance with respect to accuracy, speed, and multimodality.     

Non-local total bounded variation scheme for multiple-coil magnetic resonance image restoration

In this paper, we design a variational model for restoring multiple-coil magnetic resonance images (MRI) corrupted by non-central Chi distributed noise. The energy functional corresponding to the restoration problem is derived using the maximum a posteriori (MAP) estimator. Optimizing this functional yields the solution, which corresponds to the restored version of the image. The non-local total bounded variation prior is being used as the regularization term in the functional derived using the MAP estimation process. Further, the split-Bregman iteration scheme is being followed for fast numerical computation of the model. The results are compared with the state of the art MRI restoration models using visual representations and statistical measures.