X-ray fluoroscopy spatio-temporal filtering with object detection

One potential way to reduce patient and staff X-ray fluoroscopy dose is to reduce the quantum exposure to the detector and compensate the additional noise with digital filtering. A new filtering method, spatio-temporal filtering with object detection, is described that reduces noise while minimizing motion and spatial blur. As compared to some conventional motion-detection filtering schemes, this object-detection method incorporates additional a priori knowledge of image content; i.e. much of the motion occurs in isolated long thin objects (catheters, guide wires, etc.). We create object-likelihood images and use these to control spatial and recursive temporal filtering such as to reduce blurring the objects of interest. We use automatically computed receiver operating characteristic (ROC) curves to optimize the object-likelihood enhancement method and determine that oriented matched filter kernels with 4 orientations are appropriate. The matched filter kernels are simple projected cylinders. We demonstrate the method on several representative X-ray fluoroscopy sequences to which noise is added to simulate very low dose acquisitions. With processing, we find that noise variance is significantly reduced with slightly less noise reduction near moving objects. We estimate an effective exposure reduction greater than 80%     

Out-of-plane motion compensation in multislice spin-echo MRI

In magnetic resonance imaging (MRI), it is well-known that patient motion plays a significant role in the degradation of image quality. Although the case of translational in-plane motion (x-y-motion) has been studied by several researchers, the effect of rigid, translational out-of-plane motion (z-motion) has not yet been completely analyzed due to its more complex nature. Out-of-plane motion introduces blurring along the slice-selection direction in addition to motion artifacts. Here, the authors present a model to represent the effect of out-of-plane motion on multislice MR data. The inversion of this model not only results in the correction of the artifacts due to out-of-plane motion, but also reduces blurring in the slice-selection direction, yielding higher resolution images. Because of the shift-varying nature of the authors' model, they propose to use a nonlinear postprocessing method, projection onto convex sets (POCS), for its inversion, provided that the motion kernel and the slice-selection profile are known. The proposed method has been tested on simulated data and then applied to actual MR data to demonstrate the feasibility of the technique in real imaging situations.     

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.     

基于IMU/视觉融合的导航定位算法研究

针对基于视觉传感器的移动机器人在快速运动或发生旋转时出现图像模糊和特征丢失,以至无法进行特征匹配,从而导致系统定位和建图的准确度及精确度下降问题,该文提出了一种以深度相机(RGB_D)融合惯性测量单元（IMU）的方案。采用ORB SLAM2算法进行位姿估计,同时将IMU信息作为约束弥补相机数据的缺失。两种传感器的测量数据采用基于扩展卡尔曼滤波的松耦合方式进行非线性优化,通过数据采集实验表明,该方法能有效提高机器人的定位精度和系统建图效果。     

基于光流法的多分辨率电子稳像算法

针对动载体摄像系统中视频序列受载体姿态运动及抖动的干扰而出现的不稳定现象,提出一种基于光流算法的多分辨率电子稳像算法。首先,通过划定有效的运动估算区域取代对整帧图像的计算以降低计算量;然后,利用基于光流算法的多分辨率分层运动估计快速并精确地计算出包含平移、旋转以及缩放运动的相邻帧间仿射变换参数;最后,采用固定帧补偿算法,利用求得的仿射变换参数,对图像进行运动补偿,消除或减轻图像序列帧间的随机抖动,达到稳像的目的。实验结果表明,针对包含运动目标的动态场景,该算法可以精确地检测出视频序列帧间平移、旋转以及缩放等复杂的抖动,水平和垂直方向的稳像精确度小于1pixel,保证视频序列的稳定输出,可应用于目标跟踪系统中。     

相向运动条件下图像的辐射状退化及其复原研究

当摄像机随载体作高速运动时,由于成像曝光时间、摄像机与目标间的纵向相对运动等因素使目标图像产生从中心到边缘的辐射状模糊,使系统有效视场减少,影响对目标的探测、识别和跟踪.本文根据运动成像关系和光电探测器的信号积分机理,研究了摄像平台高速运动条件下,图像辐射状模糊的图像退化模型,对几种特殊图像的退化进行了分析;并针对实际离散成像过程,建立了图像退化模型,分析了影响辐射状退化的因素;提出了一种辐射状模糊图像的递推复原处理算法;计算机方法模拟的辐射状退化图像与实际图像一致,复原算法可有效地减轻相向运动所造成的图像辐射状模糊.     

Superresolution video reconstruction with arbitrary samplinglattices and nonzero aperture time

Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the applications that motivate superresolution (SR) image and video reconstruction from low-resolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes (i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and (ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion.     

Image Sequence Compression Using a Pel-Recursive Motion-Compensated Technique

This paper presents and analyzes a pel-recursive, motioncompensated, image sequence compression algorithm [1]. The analysis retains all the terms of the Taylor's series expansion and yields a set of equations for which the convergence criteria and the convergence rate of the motion estimate are more easily seen. The existing motion prediction schemes are also reviewed and a new motion prediction scheme is presented which is shown to be superior to the existing schemes. Simulations run on actual image sequences to verify the analytical results indicate that implementing the analytical model as opposed to the generally used heuristic technique does yield a decrease in the information rate and the computational requirements. Simulation results also are included which use the "projection-along-the-motiontrajectory" or PAMT prediction scheme. Third, zeroth-order entropy encoding is shown to reduce the bit rate on the order of 12 percent, and to reduce the mean square error in the reconstructed images on the order of 60 percent when compared to first-order entropy encoding. Fourth, field-to-field motion prediction is compared to frame-to-frame motion prediction.     

基于运动模糊效应的图像测速方法

通过分析影像与成像平面以及地面景物之间相对运动关系,建立了航拍图像运动模糊模型,分析了模糊带宽度和相机运动及曝光时间之间的关系,设计了基于模糊带边缘增强和自相关分析的模糊带宽度测量算法,在此基础上提出了基于运动模糊效应的图像测速方法.给出了实用的测速算法,并分析了像素尺寸、模糊带宽度、相机姿态等对测速精度的影响.与传统的基于多普勒、地形匹配等测速算法相比,此方法无需增加额外设备和保障数据,实现更为简单.算例分析表明,此算法对于速度无剧烈变化的准匀速运动具有较好的测速效果,为飞行器实时飞行速度的测量提供了一种新的解决手段.     

基于自适应动量估计优化器与空变最小熵准则的SAR图像船舶目标自聚焦算法

在SAR散焦船舶图像中,部分船舶目标的散焦现象具有沿距离向空变的特性.针对此类散焦船舶目标,该文提出了一种基于自适应动量估计优化器与空变最小熵准则的SAR图像船舶目标自聚焦算法,该算法直接对复图像进行处理,可以实现对任意阶次相位误差的补偿.在仿真数据和GF-3数据上的实验结果表明,所提算法可以有效地实现SAR图像空变散...     

Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation

For patient setup verification in external beam radiotherapy (EBRT) of prostate cancer, we developed an information theoretic registration framework, called the minimax entropy registration framework, to simultaneously and iteratively segment portal images and register them to three-dimensional (3-D) computed tomography (CT) image data. The registration framework has two steps, the max step and the min step, and evaluates appropriate entropies to estimate segmentations of the portal images and to find the transformation parameters. In the initial version of the algorithm (Bansal et al. 1999), we assumed image pixels to be independently distributed, an assumption not true in general. Thus, to better segment the portal images and to improve the accuracy of the estimated registration parameters, in this initial formulation of the problem, the correlation among pixel intensities is modeled using a one-dimensional Markov random process. Line processes are incorporated into the model to improve the estimation of segmentation of the portal images. In the max step, the principle of maximum entropy is invoked to estimate the probability distribution on the segmentations. The estimated distribution is then incorporated into the min step to estimate the registration parameters. Performance of the proposed framework is evaluated and compared to that of a mutual information-based registration algorithm using both simulated and real patient data. In the proposed registration framework, registration of the 3-D CT image and the portal images is guided by an estimated segmentation of the pelvic bone. However, as the prostate can move with respect to the pelvic structure, further localization of the prostate using ultrasound image data is required, an issue to be further explored in future.     

运动模糊图像点扩展函数的自动识别方法

在获取图像的过程中,传感器和场景的相对运动所造成的图像模糊是一类常见的图像退化模式。为对模糊图像进行恢复,首先要给出精确的点扩展函数估计。针对匀速运动降晰的情况,提出了一种新的误差-参数曲线法,并依据这种曲线提出了一种点扩展函数的自动估计算法。通过引入奇异因子、光滑因子以及平坦因子来实现点扩展函数的估计。实验结果表明,该方法可以取得较为精确的估计效果。     

Commutability of blur and affine warping in super-resolution with application to joint estimation of triple-coupled variables

This paper proposes a new approach to the image blind super-resolution (BSR) problem in the case of affine interframe motion. Although the tasks of image registration, blur identification, and high-resolution (HR) image reconstruction are coupled in the imaging process, when dealing with nonisometric interframe motion or without the exact knowledge of the blurring process, classic SR techniques generally have to tackle them (maybe in some combinations) separately. The main difficulty is that state-of-the-art deconvolution methods cannot be straightforwardly generalized to cope with the space-variant motion. We prove that the operators of affine warping and blur commute with some additional transforms and derive an equivalent form of the BSR observation model. Using this equivalent form, we develop an iterative algorithm to jointly estimate the triple-coupled variables, i.e., the motion parameters, blur kernels, and HR image. Experiments on synthetic and real-life images illustrate the performance of the proposed technique in modeling the space-variant degradation process and restoring local textures.     

Constrained Fourier space method for compensation of missing data in emission computed tomography

A method is introduced to compensate for missing projection data that can result from gas between detectors or from malfunctioning detectors. This method uses constraints in the Fourier domain to estimate the missing data, thus completing the data set so that the filtered backprojection algorithm can be used to reconstruct artifact-free images. The image reconstructed from estimates using this technique and a data set with gaps is nearly indistinguishable from an image reconstructed from a complete data set without gaps, using a simulated brain phantom.     

凹凸范数比值正则化的快速图像盲去模糊

模糊图像可表示为清晰图像和模糊核函数的卷积,由模糊图像恢复出清晰图像,需要同时估计模糊核和清晰图像,因此是一个病态问题.优化含有先验项的代价函数是求解病态问题最有效方法之一.针对图像盲去模糊问题,本研究提出具有更强稀疏表达能力的凹凸范数比值正则化先验项,在用变量分裂法求解模型时,提出用L1范数保真项更新估计图像,在更新模糊核时,提出使用线性递增权重参数对模糊核按多尺度方法由粗到细逐步估计,当获得模糊核后,利用封闭阈值公式估计清晰图像.该方法能快速得到高质量的清晰图像,实验结果验证了模型的有效性和算法的快速性.     

Image reconstruction by conditional entropy maximisation for PET system

The authors show that the conditional entropy maximisation algorithm is a generalised version of the maximum likelihood algorithm for positron emission tomography (PET). Promising properties of the conditional entropy maximisation algorithm are as follows: an assumption is made that the entropy of the information content of the data should be maximised; it is a consistent way of selecting an image from the very many images that fit the measurement data; this approach takes care of the positivity of the reconstructed image pixels, since entropy does not exist for negative image pixel values; and inclusion of prior distribution knowledge in the reconstruction process is possible. Simulated experiments performed on a PET system have shown that the quality of the reconstructed image using the entropy maximisation method is good. A Gibbs distribution is used to incorporate prior knowledge into the reconstruction process. The mean squared error (MSE) of the reconstructed images shows a sharp new dip, confirming improved image reconstruction. The entropy maximisation method is an alternative approach to maximum likelihood (ML) and maximum a posteriori (MAP) methodologies.     

Estimating fractal dimension with fractal interpolation functionmodels

Fractal dimension (FD) is a feature which is widely used to characterize medical images. Previously, researchers have shown that FD separates important classes of images and provides distinctive information about texture. The authors analyze limitations of two principal methods of estimating FD: box-counting (BC) and power spectrum (PS). BC is ineffective when applied to data-limited, low-resolution images; PS is based on a fractional Brownian motion (fBm) model-a model which is not universally applicable. The authors also present background information on the use of fractal interpolation function (FIF) models to estimate FD of data which can be represented in the form of a function. They present a new method of estimating FD in which multiple FIF models are constructed. The mean of the FD's of the FIF models is taken as the estimate of the FD of the original data. The standard deviation of the FD's of the FIF models is used as a confidence measure of the estimate. The authors demonstrate how the new method can be used to characterize fractal texture of medical images. In a pilot study, they generated plots of curvature values around the perimeters of images of red blood cells from normal and sickle cell subjects. The new method showed improved separation of the image classes when compared to BC and PS methods     

Neural fuzzy motion estimation and compensation

Neural fuzzy systems can improve motion estimation and compensation for video compression. Motion estimation and compensation are key parts of video compression. They help remove temporal redundancies in images. But most motion estimation algorithms neglect the strong temporal correlations of the motion field. The search windows stay the same through the image sequences and the estimation needs heavy computation. A neural vector quantizer system can use the temporal correlation of the motion field to estimate the motion vectors. First- and second-order statistics of the motion vectors give ellipsoidal search windows. This algorithm reduced the search area and entropy and gave clustered motion fields. Motion-compensated video coding further assumes that each block of pixels moves with uniform translational motion. This often does not hold and can produce block artifacts. We use a neural fuzzy system to compensate for the overlapped block motion. This fuzzy system uses the motion vectors of neighboring blocks to map the prior frame's pixel values to the current pixel value. The neural fuzzy system used 196 rules that came from the prior decoded frame. The fuzzy system learns and updates its rules as it decodes the image. The fuzzy system also improved the compensation accuracy. The paper derives both the fuzzy system and the neural learning laws that tune its parameters     

Slope-based frame selection and scaling technique for ship ISAR imaging

The authors address the problem of optimum imaging time selection and angular motion estimation for ship ISAR or hybrid SAR/ISAR imaging. The aim is to select proper imaging times and to estimate ship angular motion in order to obtain high-quality top view or side view scaled images of the ship, suitable for processing by classification/identification procedures. For this purpose, a new technique is proposed, able to select the time instants better suited for top or side view image formation and to estimate the rotation motion for image scaling. The technique is based on a novel model of the phase/Doppler frequency of the generic ship scatterer and operates entirely in the range-Doppler image domain (as obtained via Fourier transform) by using the slopes of some extracted linear features, thus without requiring coherent processing. The performance of the complete ISAR technique is analysed in depth; the results of the application of the proposed technique to both simulated and live ISAR data prove the effectiveness of the proposed approach.     

Respiratory motion correction in 3-D PET data with advanced optical flow algorithms

The problem of motion is well known in positron emission tomography (PET) studies. The PET images are formed over an elongated period of time. As the patients cannot hold breath during the PET acquisition, spatial blurring and motion artifacts are the natural result. These may lead to wrong quantification of the radioactive uptake. We present a solution to this problem by respiratory-gating the PET data and correcting the PET images for motion with optical flow algorithms. The algorithm is based on the combined local and global optical flow algorithm with modifications to allow for discontinuity preservation across organ boundaries and for application to 3-D volume sets. The superiority of the algorithm over previous work is demonstrated on software phantom and real patient data.