Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

Reconstructing an accurate and topologically correct representation of the cortical surface of the brain is an important objective in various neuroscience applications. Most cortical surface reconstruction methods either ignore topology or correct it using manual editing or methods that lead to inaccurate reconstructions. Shattuck and Leahy recently reported a fully automatic method that yields a topologically correct representation with little distortion of the underlying segmentation. We provide an alternate approach that has several advantages over their approach, including the use of arbitrary digital connectivities, a flexible morphology-based multiscale approach, and the option of foreground-only or background-only correction. A detailed analysis of the method's performance on 15 magnetic resonance brain images is provided.     

Automated motion correction for in vivo optical projection tomography

In in vivo optical projection tomography (OPT), object motion will significantly reduce the quality and resolution of the reconstructed image. Based on the well-known Helgason-Ludwig consistency condition (HLCC), we propose a novel method for motion correction in OPT under parallel beam illumination. The method estimates object motion from projection data directly and does not require any other additional information, which results in a straightforward implementation. We decompose object movement into translation and rotation, and discuss how to correct for both translation and general motion simultaneously. Since finding the center of rotation accurately is critical in OPT, we also point out that the system's geometrical offset can be considered as object translation and therefore also calibrated through the translation estimation method. In order to verify the algorithm effectiveness, both simulated and in vivo OPT experiments are performed. Our results demonstrate that the proposed approach is capable of decreasing movement artifacts significantly thus providing high quality reconstructed images in the presence of object motion.     

Automated spectrum analysis

Automatic spectrum analyzers (ASA's) combine a powerful RF measurement system with computer control and data processing. This article discusses a typical ASA block diagram (analog and digital subsystems), shows how the calibration procedures used in such systems enhance the accuracy of measurements, and discusses some of the problems with current ASA systems. The present use of ASA systems in government and industry is illustrated with several examples of ASA systems used in production testing of large electronic systems, measurements of the electromagnetic environment, communication satellite monitoring, and EMC testing. Finally, alternate technologies for making spectrum measurements and the future of ASA's are discussed.     

Automated model-based bias field correction of MR images of the brain

We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms.     

Automated cardiac dysrhythmia analysis

Automated analysis of abnormal cardiac rhythms (dysrhythmias) is well established for use in real-time ECG monitoring in hospital intensive-care units and for high-speed processing of long-term ambulatory ECG recordings; yet considerable difficulties persist. The many facets of ECG signal acquisition have not been sufficiently well standardized, with a result that definitive signal characterization continues to be troublesome. Analysis algorithms rely heavily on time-domain feature extraction or correlation techniques, although incursions have been made into other domains. Progress continues to be made in improving analysis accuracy, but no algorithm is without its weaknesses. Most system implementations employ some degree of human interaction to compensate for analysis deficiencies. Performance evaluation of implemented systems requires extensive effort, and results to date are clouded by a lack of evaluation standards and the absence of a widely accepted evalution database. The American Heart Association is sponsoring work which promises to put future evaluations on firmer ground. Research continues to address all of these issues because of a strong belief in the clinical utility of automated dysrhythmia analysis. The rationale for that belief is clearer for the analysis of long-term ECG recordings than it is for in-hospital monitoring, but results are available to show that patients are treated more vigorously if such monitoring is employed, and newer therapeutic approches have increased the importance of reliably detecting rare but significant events.     

Automated analysis of phased-mission reliability

A method for automated analysis of phased mission reliability which considers the problem in terms of the construction of a continuous-time discrete-state Markov model and uses a standard Markov-chain solution technique that is adapted to the problem of phased missions is presented. The resulting state space is the union of the states in each independent phase, rather than the sum. This results in a model that can be substantially smaller than those required by other methods. A unified framework which is used for defining the separate phases using fault trees and for constructing and solving the resulting Markov model is discussed. The usual solution technique is altered to account for the phased nature of the problem. The framework is described for a previously published, simple three-component, three-phase system. An example in which a hypothetical two-phase application involving a fault-tolerant parallel processor is considered is given. The approach applies where the transition rates (failure and repair rates) are constant and where the phase change times are deterministic     

Automated extraction and variability analysis of sulcalneuroanatomy

Systematic mapping of the variability in cortical sulcal anatomy is an area of increasing interest which presents numerous methodological challenges. To address these issues, we have implemented sulcal extraction and assisted labeling (SEAL) to automatically extract the two-dimensional (2-D) surface ribbons that represent the median axis of cerebral sulci and to neuroanatomically label these entities. To encode the extracted three-dimensional (3-D) cortical sulcal schematic topography (CSST) we define a relational graph structure composed of two main features: vertices (representing sulci) and arcs (representing the relationships between sulci). Vertices contain a parametric representation of the surface ribbon buried within the sulcus. Points on this surface are expressed in stereotaxic coordinates (i.e., with respect to a standardized brain coordinate system). For each of these vertices, we store length, depth, and orientation as well as anatomical attributes (e.g., hemisphere, lobe, sulcus type, etc.). Each arc stores the 3-D location of the junction between sulci as well as a list of its connecting sulci. Sulcal labeling is performed semiautomatically by selecting a sulcal entity in the CSST and selecting from a menu of candidate sulcus names. In order to help the user in the labeling task, the menu is restricted to the most likely candidates by using priors for the expected sulcal spatial distribution. These priors, i.e., sulcal probabilistic maps, were created from the spatial distribution of 34 sulci traced manually on 36 different subjects. Given these spatial probability maps, the user is provided with the likelihood that the selected entity belongs to a particular sulcus. The cortical structure representation obtained by SEAL is suitable to extract statistical information about both the spatial and the structural composition of the cerebral cortical topography. This methodology allows for the iterative construction of a successively more complete statistical models of the cerebral topography containing spatial distributions of the most important structures, their morphometrics, and their structural components.     

Automated extraction of the cortical sulci based on a supervised learning approach

It is important to detect and extract the major cortical sulci from brain images, but manually annotating these sulci is a time-consuming task and requires the labeler to follow complex protocols. This paper proposes a learning-based algorithm for automated extraction of the major cortical sulci from magnetic resonance imaging (MRI) volumes and cortical surfaces. Unlike alternative methods for detecting the major cortical sulci, which use a small number of predefined rules based on properties of the cortical surface such as the mean curvature, our approach learns a discriminative model using the probabilistic boosting tree algorithm (PBT). PBT is a supervised learning approach which selects and combines hundreds of features at different scales, such as curvatures, gradients and shape index. Our method can be applied to either MRI volumes or cortical surfaces. It first outputs a probability map which indicates how likely each voxel lies on a major sulcal curve. Next, it applies dynamic programming to extract the best curve based on the probability map and a shape prior. The algorithm has almost no parameters to tune for extracting different major sulci. It is very fast (it runs in under 1 min per sulcus including the time to compute the discriminative models) due to efficient implementation of the features (e.g., using the integral volume to rapidly compute the responses of 3-D Haar filters). Because the algorithm can be applied to MRI volumes directly, there is no need to perform preprocessing such as tissue segmentation or mapping to a canonical space. The learning aspect of our approach makes the system very flexible and general. For illustration, we use volumes of the right hemisphere with several major cortical sulci manually labeled. The algorithm is tested on two groups of data, including some brains from patients with Williams Syndrome, and the results are very encouraging.     

Automated correction of patient motion and gray values prior to subtraction in digitized angiography

This paper deals with an automated method for the simultaneous correction of patient motion and gray values prior to subtraction in digitized angiography. The algorithm consists of maximizing the deterministic sign change (DSC) criterion with respect to three registration parameters (two translational shifts and one constant value added to the pixel values of the image with contrast medium). This method is proved to be very efficient to correct for patient motion artifacts and is computationally cheap. Its main advantage is to permit the use of regions of interest which include vascular structures for the registration procedure. This method can be proposed for a routine use on every commercial digitized angiographic system.     

Automated correction of spin-history related motion artefacts in fMRI: simulated and phantom data

This paper concerns the problem of correcting spin-history artefacts in fMRI data. We focus on the influence of through-plane motion on the history of magnetization. A change in object position will disrupt the tissue's steady-state magnetization. The disruption will propagate to the next few acquired volumes until a new steady state is reached. In this paper we present a simulation of spin-history effects, experimental data, and an automatic two-step algorithm for detecting and correcting spin-history artefacts. The algorithm determines the steady-state distribution of all voxels in a given slice and indicates which voxels need a spin-history correction. The spin-history correction is meant to be applied before standard realignment procedures. To obtain experimental data a special phantom and an MRI compatible motion system were designed. The effect of motion on spin-history is presented for data obtained using this phantom inside a 1.5-T MRI scanner. We show that the presented algorithm is capable of detecting the occurrence of a displacement, and it determines which voxels need a spin-history correction. The results of the phantom study show good agreement with the simulations.     

Automated analysis of repetitive joint motion

Automated measurement, analysis, and comparison of human motion during performance of workplace tasks or exercise therapy are core competencies required to realize many telemedicine applications. Ergonomic studies and telemonitoring of patients performing rehabilitation exercises are examples of applications that would benefit from a representation of complex human motion in a form amenable to comparison. We present a representation of joint motion suitable for the analysis of multidimensional angular joint motion time series data. Complex motion is reduced to a concatenation motion segments, where simple dynamic models approximate the observed motion on each segment. This compact representation still enables measurement of statistics familiar to ergonomics practitioners such as cycle length and task duration. An algorithm to obtain this representation from observed motion data (time series) is given. We introduce a metric, based on a kinetic energy-like measure, to compare motions. Experiments are presented to demonstrate the representation, its relationship to previous measures and the applicability of the kinetic energy metric for motion comparison.     

Automated measurement and analysis of MIS interfaces in narrow-bandgap semiconductors

A computerized system for simultaneous measurement of high-frequency and quasi-static MIS capacitance is described. The importance of this simultaneity in the application of various analysis methods to MIS devices in narrow-bandgap semiconductors is discussed. Results on the interfaces of mercury-cadmium-telluride with zinc sulfide and anodic oxide are given.     

Automated analysis of Doppler ultrasound velocity flow diagrams

A highly automated method for the identification and quantization of maximum blood velocity curves from Doppler ultrasound flow diagrams is presented. The method uses an image processing scheme to analyze video-recorded image sequences of flow diagrams. The sequences are acquired, a sequence of images relating to chronological cardiac cycles is extracted, and a maximum blood velocity envelope is determined and quantified. The results are verified against hand-traced reference curves. Excellent correlation of r = 0.99 is achieved.     

Automated high-speed analysis of Holter tapes with microcomputers

We have developed an automated Holtes scanning system based on two microcomputers. One is a preprocessor that detects QRS complexes and measures the QRS durations using computations of first and second derivatives. Thismicrocomputer interfaces to a secondmicro-computer that does arrhythmia analysis, logging, and reporting using R-R intervals and QRS durations. This system can process Holter tapes at 60 times real time and produce printed summaries and 24 h trend plots of several variables including heart rate and PVC count.     

Salient region detection through sparse reconstruction and graph-based ranking

In this paper, we propose a salient region detection algorithm from the point of view of unique and compact representation of individual image. In first step, the original image is segmented into super-pixels. In second step, the sparse representation measure and uniqueness of the features are computed. Then both are ranked on the basis of the background and foreground seeds respectively. Thirdly, a location prior map is used to enhance the foci of attention. We apply the Bayes procedure to integrate computed results to produce smooth and precise saliency map. We compare our proposed algorithm against the state-of-the-art saliency detection methods using four of the largest widely available standard data-bases, experimental results specify that the proposed algorithm outperforms. We also show that how the saliency map of the proposed method is used to discover outline of object, furthermore using this outline our method produce the saliency cut of the desired object.     

气动光学效应校正技术初步分析

为了对付空中新一代威胁,未来的防空导弹具有高速高加速,高精度光学探测和侧窗探测与控制等特点。文章分析了新一代防空导弹红外成像导引头侧窗探测气动光学效应产生的机理,并结合弹上末制导的特点,在对国内外文献综合分析的基础上,提出了气动光学效应校正技术途径,主要包括：焦面优化技术,自适应变帧频变积分时间的快速红外成像技术,背抑制技术和综合模糊识别技术等。     

密钥交换协议前向安全性的自动化分析

会话密钥的安全影响了整个通信网络的安全,前向安全性是密钥交换协议中保证会话密钥安全的一种特殊的安全属性。首先扩展了应用PI演算,增加了阶段进程语法描述协议的前向安全性;然后提出了一个基于一阶定理证明器Pro Verif的前向安全性自动化分析方法;最后运用这种方法分析了两种典型的密钥交换协议,STS协议和MTI协议的前向安全性,分析结果表明该方法简单可靠。     

Automated quantification of the synchrogram by recurrence plot analysis

Recently, the concept of phase synchronization of two weakly coupled oscillators has raised a great research interest and has been applied to characterize synchronization phenomenon in physiological data. Phase synchronization of cardiorespiratory coupling is often studied by a synchrogram analysis, a graphical tool investigating the relationship between instantaneous phases of two signals. Although several techniques have been proposed to automatically quantify the synchrogram, most of them require a preselection of a phase-locking ratio by trial and error. One technique does not require this information; however, it is based on the power spectrum of phase's distribution in the synchrogram, which is vulnerable to noise. This study aims to introduce a new technique to automatically quantify the synchrogram by studying its dynamic structure. Our technique exploits recurrence plot analysis, which is a well-established tool for characterizing recurring patterns and nonstationarities in experiments. We applied our technique to detect synchronization in simulated and measured infants' cardiorespiratory data. Our results suggest that the proposed technique is able to systematically detect synchronization in noisy and chaotic data without preselecting the phase-locking ratio. By embedding phase information of the synchrogram into phase space, the phase-locking ratio is automatically unveiled as the number of attractors.     

A quantitative comparison of graph-based models for Internettopology

Graphs are commonly used to model the topological structure of internetworks in order to study problems ranging from routing to resource reservation. A variety of graphs are found in the literature, including fixed topologies such as rings or stars, “well-known” topologies such as the ARPAnet, and randomly generated topologies. While many researchers rely upon graphs for analytic and simulation studies, there has been little analysis of the implications of using a particular model or how the graph generation method may affect the results of such studies. Further, the selection of one generation method over another is often arbitrary, since the differences and similarities between methods are not well understood. This paper considers the problem of generating and selecting graphs that reflect the properties of real internetworks. We review generation methods in common use and also propose several new methods. We consider a set of metrics that characterize the graphs produced by a method, and we quantify similarities and differences among several generation methods with respect to these metrics. We also consider the effect of the graph model in the context of a specific problem, namely multicast routing