Joint kinematics simulation from medical imaging data

A method for joint kinematics simulation is described. Kinematics parameters are determined from the relative displacement of marker sets placed on anatomical landmarks of surface models generated from medical imaging contour data. The landmarks are identified manually on fingers in multiple positions. A mathematical algorithm was then used to ascertain the kinematics axes of motion of the fingers. Once these axes are located, they are used as the base of a real time interactive simulation of the finger. The entire simulation was accomplished in a high-resolution graphics environment. A full complement of interactive tools (virtual dials and buttons controlled via mouse) was used to enhance the user interface. The development of the system, the model and the advantages and disadvantages of the method are discussed     

Construction of a human topological model from medical data

Medical imaging can provide data for useful views of the interior details of human anatomy. In addition to visualization, which in general has been the primary reason for obtaining these data, many other uses are possible. These include modeling of different elements and their inter-relationships -topological modeling, simulation of physical processes, analysis of movements and validation of models. In this paper, we describe some of the modeling issues from medical imaging. The issues are particularly related to topological modeling of different anatomical elements: bones, muscles, articulations, etc. A 3D topological modeler is presented with which anatomists and other users can build a topological database containing structural, topological and mechanical information of anatomical elements     

Multiscale Bayesian segmentation using a trainable context model

Multiscale Bayesian approaches have attracted increasing attention for use in image segmentation. Generally, these methods tend to offer improved segmentation accuracy with reduced computational burden. Existing Bayesian segmentation methods use simple models of context designed to encourage large uniformly classified regions. Consequently, these context models have a limited ability to capture the complex contextual dependencies that are important in applications such as document segmentation. We propose a multiscale Bayesian segmentation algorithm which can effectively model complex aspects of both local and global contextual behavior. The model uses a Markov chain in scale to model the class labels that form the segmentation, but augments this Markov chain structure by incorporating tree based classifiers to model the transition probabilities between adjacent scales. The tree based classifier models complex transition rules with only a moderate number of parameters. One advantage to our segmentation algorithm is that it can be trained for specific segmentation applications by simply providing examples of images with their corresponding accurate segmentations. This makes the method flexible by allowing both the context and the image models to be adapted without modification of the basic algorithm. We illustrate the value of our approach with examples from document segmentation in which test, picture and background classes must be separated.     

高光谱遥感数据吸收位置特征融合提取技术研究

光谱曲线形状与目标性质是对应的,通常以吸收峰位置作为高光谱图像中目标的波谱特征并进行比较。为解决高光谱图像中目标组成均质性差所带来的光谱特性表征和描述困难,基于单一地物的波谱响应特征具有内在一致性这一特点,提出了吸收峰位置融合提取模型。应用事例说明,该方法对高光谱图像中目标波谱特性的统一描述具有较高的实用价值。     

Speech-driven facial animation using a hierarchical model

A system capable of producing near video-realistic animation of a speaker given only speech inputs is presented. The audio input is a continuous speech signal, requires no phonetic labelling and is speaker-independent. The system requires only a short video training corpus of a subject speaking a list of viseme-targeted words in order to achieve convincing realistic facial synthesis. The system learns the natural mouth and face dynamics of a speaker to allow new facial poses, unseen in the training video, to be synthesised. To achieve this the authors have developed a novel approach which utilises a hierarchical and nonlinear principal components analysis (PCA) model which couples speech and appearance. Animation of different facial areas, defined by the hierarchy, is performed separately and merged in post-processing using an algorithm which combines texture and shape PCA data. It is shown that the model is capable of synthesising videos of a speaker using new audio segments from both previously heard and unheard speakers.     

Sleep scoring using polysomnography data features

The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The data analyzed represent polysomnographic records of (i) 33 healthy individuals, (ii) 25 individuals with sleep apnea, and (iii) 18 individuals with sleep apnea and restless leg syndrome. The initial statistical analysis of the sleep segments points to an increase in the number of Wake stages and the decrease in REM stages with increase in age. The goal of the study is visualization of features associated with sleep stages as specified by an experienced neurologist and in their adaptive classification. The results of the support vector machine classifier are compared with those obtained by the k-nearest neighbors method, decision tree and neural network classification using sigmoidal and Bayesian transfer functions. The achieved accuracy for the classification into two classes (to separate the Wake stage from one of NonREM and REM stages) is between 85.6 and 97.5% for the given set of patients with sleep apnea. The proposed models allow adaptive modification of the model coefficients during the learning process to increase the diagnostic efficiency of sleep disorder analysis, in both the clinical and home environments.     

基于DEM的山脉线提取方法研究

体现山脉宏观走势和保持山脉线连续性是山脉线提取的难点。文中认为山脉线是由主山脊线片段、山顶点及鞍部点按照一定规则连接而成。先利用栅格DEM低通滤波后提取矢量等高线,再结合DEM提取出山脊线,通过筛选山脊线得到主山脊线来保持山脉宏观走势;再通过主山脊线的二次连接以及将鞍部点、山顶点连接到主山脊线形成连续的山脉线。实验结果表明:这种利用不同类型的数据,地形特征点、线相结合提取山脉线的方法获取的山脉线走势合理、连续性较好,符合人们地貌认知。     

A subrefractive fading model for microwave paths using surface synoptic meteorological data

A meteorological-based model of the subrefractive fading mechanism is developed. A previous investigation supplied refractive index gradients along a test path at various times during subrefractive fading events. The lowest (worst) signal levels corresponded to linear subrefractive gradients. These linear gradients can be considered using the relative ray curvature between the ray and the earth's surface, and are described simply by a parameterk. Using a basic model of the meteorological process causing this subrefraction, a lower bound onkcan be estimated. Examples of this estimation procedure are given and compared with minimumkvalues derived from microwave fading. A method of using these minimumkvalues to derive subrefraction statistics is the subject of a companion paper.     

Integration of hierarchical test generation with behavioralsynthesis of controller and data path circuits

This paper describes the first behavioral synthesis system that incorporates a hierarchical test generation approach to synthesize area-efficient and highly testable controller/data path circuits. Functional information of circuit modules is used during the synthesis process to facilitate complete and easy testability of the data path. The controller behavior is taken into account while targeting data path testability. No direct controllability of the controller outputs through scan or otherwise is assumed. The test set for the combined controller/data path is generated during synthesis in a very short time. Near 100% testability of combined controller and data path is achieved. The synthesis system easily handles large bit-width data path circuits with sequential loops and conditional branches in their behavioral specification, and scheduling constructs like multicycling, chaining and structural pipelining. An improvement of about three to four orders of magnitude was usually obtained in the test generation time for the synthesized benchmarks as compared to an efficient gate-level sequential test generator. The testability overheads are almost zero. Furthermore, in many cases at-speed testing is also possible     

MRF model based image segmentation using hierarchical distributedgenetic algorithm

An unsupervised method for segmenting noisy and blurred images is proposed. A Markov random field (MRF) model is used which is robust to degradation. Since this is computationally intensive, a hierarchical distributed genetic algorithm (HDGA) is used which is unsupervised and parallel. Experimental results show that the proposed method is effective at segmenting real images     

Multiscale FEM modeling of vascular tone: from membrane currents to vessel mechanics

Regulation of vascular tone is a complex process that remains poorly understood. Here, we present our recent efforts for the development of physiologically realistic models of arterial segments for the analysis of vasoreactivity in health and disease. Multiscale modeling integrates intracellular and cell membrane components into whole-cell models of calcium and membrane potential dynamics. Single-cell models of vascular cells are combined into a multicellular model of the vascular wall, and vessel wall biomechanics are integrated with calcium dynamics in the smooth muscle layer. At each scale, continuum models using finite element method can account for spatial heterogeneity in calcium signaling and for nonuniform deformations of a vessel segment. The outlined approach can be used to investigate cellular mechanisms underlying altered vasoreactivity in hypertension.     

Fast maximum intensity projections of large medical data sets by exploiting hierarchical memory architectures.

Maximum intensity projections (MIPs) are an important visualization technique for angiographic data sets. Efficient data inspection requires frame rates of at least five frames per second at preserved image quality. Despite the advances in computer technology, this task remains a challenge. On the one hand, the sizes of computed tomography and magnetic resonance images are increasing rapidly. On the other hand, rendering algorithms do not automatically benefit from the advances in processor technology, especially for large data sets. This is due to the faster evolving processing power and the slower evolving memory access speed, which is bridged by hierarchical cache memory architectures. In this paper, we investigate memory access optimization methods and use them for generating MIPs on general-purpose central processing units (CPUs) and graphics processing units (GPUs), respectively. These methods can work on any level of the memory hierarchy, and we show that properly combined methods can optimize memory access on multiple levels of the hierarchy at the same time. We present performance measurements to compare different algorithm variants and illustrate the influence of the respective techniques. On current hardware, the efficient handling of the memory hierarchy for CPUs improves the rendering performance by a factor of 3 to 4. On GPUs, we observed that the effect is even larger, especially for large data sets. The methods can easily be adjusted to different hardware specifics, although their impact can vary considerably. They can also be used for other rendering techniques than MIPs, and their use for more general image processing task could be investigated in the future.     

Three-dimensional surface reconstruction using optical flow formedical imaging

The recovery of a three-dimensional (3-D) model from a sequence of two-dimensional (2-D) images is very useful in medical image analysis. Image sequences obtained from the relative motion between the object and the camera or the scanner contain more 3-D information than a single image. Methods to visualize the computed tomograms can be divided into two approaches: the surface rendering approach and the volume rendering approach. In this paper, a new surface rendering method using optical flow is proposed. Optical flow is the apparent motion in the image plane produced by the projection of real 3-D motion onto the 2-D image. The 3-D motion of an object can be recovered from the optical-flow field using additional constraints. By extracting the surface information from 3-D motion, it is possible to obtain an accurate 3-D model of the object. Both synthetic and real image sequences have been used to illustrate the feasibility of the proposed method. The experimental results suggest that the proposed method is suitable for the reconstruction of 3-D models from ultrasound medical images as well as other computed tomograms     

Genetic approach to select wavelet features for contour extractionin medical ultrasonic imaging

An efficient and robust approach, which is based on wavelet transform (WT), is proposed for contour extraction for medical ultrasonic images having low signal-to-noise ratio. Furthermore the best wavelet features for profile analysis is estimated by GAs without manual operation. No image preprocessing is needed, so computation time is fast. Experimental results to confirm the proposed algorithm are also included     

A novel hierarchical data association with dynamic viewpoint model for multiple targets tracking

A new framework of hierarchical data association tracking (HDAT) with branch partition, candidate upgrading and incremental motion pairing inference is proposed to resolve the problem of online multiple targets tracking. Branch partition divides the process into several independent parts so as to reduce the computational complexity on affinity. Candidate upgrading improves the robustness of target initialization by tracking potential targets and incremental motion pairing inference could benefit the occlusion handling. Furthermore, a dynamic viewpoint model (DVM) and its iterative computation algorithm are developed for tracking multiple targets under moving camera videos. Extensive data experiments on several public benchmarks show that the presented approach achieves comparable results to state-of-the-art on static camera videos and promising results on moving camera videos, and moreover, the runtime performance is significantly improved.     

Minority carrier generation time and surface generation velocity determination from Q-t measurements

The time variation of the charge stored in an MOS capacitor during the transient period following the application of a step voltage is related to the minority-carrier generation mechanisms both in the bulk and in the surface. Therefore, the minority-carrier generation time and surface generation velocity can be determined by measuring the charge as a function of time. The method has been shown to be applicable to samples with both uniform and nonuniform doping concentrations. This technique does not require that the amplitude of the step voltage be kept small. Values of lifetime and surface generation velocity obtained by this technique are shown to agree well with those obtained by theC-ttechnique.     

A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS

Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed.     

区域医疗影像数据中心的设计与建设

通过医院影像数据中心的设计与实现,缓解患者重复检查、重复收费,解决看病难、看病贵的实际问题。制定医学影像远程会诊服务和相关机制,重点建设需联网医院的PACS系统。实现"医学影像数据中心"的双向集成,实现协作医院的患者在当地医院就诊、拍片后,就可享受医学影像远程会诊的专家级诊断服务。通过统一的"区域化影像信息网络平台接口"与本市"区域级医学影像中心"进行联网,优化和高度共享区域内医学影像资源,降低影像检查成本,实现医疗资源优化合理配置。     

Analysis of fMRI data using improved self-organizing mapping and spatio-temporal metric hierarchical clustering

The self-organizing mapping (SOM) and hierarchical clustering (HC) methods are integrated to detect brain functional activation; functional magnetic resonance imaging (fMRI) data are first processed by SOM to obtain a primary merged neural nodes image, and then by HC to obtain further brain activation patterns. The conventional Euclidean distance metric was replaced by the correlation distance metric in SOM to improve clustering and merging of neural nodes. To improve the use of spatial and temporal information in fMRI data, a new spatial distance (node coordinates in the 2-D lattice) and temporal correlation (correlation degree of each time course in the exemplar matrix) are introduced in HC to merge the primary SOM results. Two simulation studies and two in vivo fMRI data that both contained block-design and event-related experiments revealed that brain functional activation can be effectively detected and that different response patterns can be distinguished using these methods. Our results demonstrate that the improved SOM and HC methods are clearly superior to the statistical parametric mapping (SPM), independent component analysis (ICA), and conventional SOM methods in the block-design, especially in the event-related experiment, as revealed by their performance measured by receiver operating characteristic (ROC) analysis. Our results also suggest that the proposed new integrated approach could be useful in detecting block-design and event-related fMRI data.