基于形状相似性和曲线化简的统计形状模型建立

针对活动形状模型(ASM)中统计模型建立存在的标记点的自动选取和点的对应两大问题,提出了一个完整的解决方案:首先通过矩阵的相似变换对齐训练集中各样本,然后根据样本特征,选择基准样本,再利用曲线化简方法在基准样本上自动提取特征点,最后利用训练样本间形状的相似性,由基准样本上的特征点向其它样本投影得到所有样本卜的标记点,实现了标记点的自动选取.同时保证了样本间标记点的一一对应.为了验证该方法的精确性和有效性.分别建立股骨和髋臼骨的点分布模型,实验结果表明,模型形状变化表现力强、计算效率高.分别与传统的手工标记法和优化法比较,在具有相同形状表现力的情况下,本文方法建模效率更高,时间开销更小.     

基于统计模型的三线阵CCD月球影像快速匹配算法

为实现快速鲁棒的同名像点匹配,基于概率统计特征,提出一种针对CCD影像进行快速匹配的方法。首先应用统计方法建立月表整体形状模型和特征形状模型并构建轮廓特征;然后提取轮廓上的关键点作为特征点,随机选择若干特征点对得到候选变换;最后对其变换参数进行检验和求精。实验结果表明,本文算法比传统匹配算法的速度有了较大的提高,能够在...     

基于统计模型的三线阵CCD影像并行匹配算法

在基于统计方法实现CCD影像匹配算法的基础上,提出了CCD影像匹配计算任务的分解方法和计算任务的调度策略,进一步构建了基于统计模型的三线阵CCD影像匹配算法的并行计算方案。并通过实验进行了验证,结果表明,对于CCD影像该并行匹配算法获得了良好的加速比,可扩展性好。适用于超大规模的三线阵CCD影像中同名像点的匹配计算。     

Venous catheter based mapping of ectopic epicardial activation: training data set selection for statistical estimation

A source of error in most of the existing catheter cardiac mapping approaches is that they are not capable of acquiring epicardial potentials even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this subgroup of patients, mapping techniques that are limited to the endocardium result in localization errors and failure in subsequent ablation procedures. In addition, catheter-based electrophysiological studies of the epicardium are limited to regions near the coronary vessels or require transthoracic access. We have developed a statistical approach by which to estimate high-resolution maps of epicardial activation from very low-resolution multi-electrode venous catheter measurements. A training set of previously recorded maps is necessary for this technique so that composition of the database becomes an important determinant of accuracy. The specific hypothesis of the study was that estimation accuracy would be best when the training data set matches that of the test beat(s), whereby the matching was according to the site of initiation of the beats. This hypothesis suggests approaches to optimized selection of the training set, three of which we have developed and evaluated. One of these methods, the high-CC refinement method, was able to estimate the earliest activation site of left ventricularly paced maps within an average of 4.67 mm of the true site; in 89% of the cases (a total of 231 cases) the error was smaller than 10 mm. In another method, MHC-Spatial activation, right ventricularly paced maps (239 maps) were estimated with an error of 7.15 mm. The average correlation coefficient between the original and the estimated maps was also very high (0.97), which shows the ability of the training data set refinement methods to estimate the epicardial activation sequence. The results of these tests support the hypothesis and, moreover, suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting.     

Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI

The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail. The aim of this hierarchical decomposition is twofold: to efficiently characterize the relationships between objects and their particular localities. Experiments performed on an eight-object structure defined in axial cross sectional MR brain images show that the new hierarchical segmentation significantly improves the accuracy of the segmentation, and while it exhibits a remarkable robustness with respect to the size of the training set.     

Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution B-spline deformations. This approach presents some advantages over previously published methods: it can treat multiple-part structures and requires less restrictive assumptions on the structure's topology. In this paper, we address the problem of building a 3-D statistical shape model of the left and right ventricle of the heart from 3-D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects.     

Detecting reduced bone mineral density from dental radiographs using statistical shape models.

We describe a novel method of estimating reduced bone mineral density (BMD) from dental panoramic tomograms (DPTs), which show the entire mandible. Careful expert width measurement of the inferior mandibular cortex has been shown to be predictive of BMD in hip and spine osteopenia and osteoporosis. We have implemented a method of automatic measurement of the width by active shape model search, using as training data 132 DPTs of female subjects whose BMD has been established by dual-energy X-ray absorptiometry. We demonstrate that widths measured after fully automatic search are significantly correlated with BMD, and exhibit less variability than manual measurements made by different experts. The correlation is highest towards the lateral region of the mandible, in a position different from that previously employed for manual width measurement. An receiver-operator characterstic (ROC) analysis for identifying osteopenia (T < -1: BMD more than one standard deviation below that of young healthy females) gives an area under curve (AUC) value of 0.64. Using a minimal interaction to initiate active shape model (ASM) search, the measurement can be made at the optimum region of the mandible, resulting in an AUC value of 0.71. Using an independent test set, AUC for detection of osteoporosis (T < -2.5) is 0.81.     

Compound statistical models for shadowed fading channels

The commonly used distribution for shadowed fading channels, the Nakagami-lognormal distribution, arises by compounding the Nakagami and log-normal distributions. This distribution, however, does not have a closed form and recently this led Shankar [Outage probabilities in shadowed fading channels using a compound statistical model. IEE Proc Commun 2005; 152:828–32; Error rates in generalized shadowed fading channels. Wireless Personal Commun 2004; 28:233–8; A compound scattering pdf for the ultrasonic echo envelope and its relationship to K and Nakagami distributions. IEEE Trans Ultrason Ferroelectr Frequency Control 2003; 50:339–43.] to propose an alternative form obtained by compounding the Nakagami and gamma distributions. In this note, we derive a comprehensive collection of models for shadowed fading channels obtained by compounding the Nakagami distribution with 16 flexible families. The calculations involve use of several special functions and their properties. We feel that this work could serve as a useful reference for statistical modeling of shadowed fading channels.     

An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures

This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warranty that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.     

Learning active shape models for bifurcating contours

Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. Automatic segmentation of tibial and femoral contours in knee X-ray images is investigated as a step towards reliable, quantitative radiographic analysis of osteoarthritis for diagnosis and assessment of progression. Results are presented using various features, the Mahalanobis distance, distance weighted K-nearest neighbours, and two relevance vector machine-based methods as quality of fit measure.     

A statistical method for obtaining the factors inelectronic-component reliability-prediction models

According to US Mil-Hdbk-217, the failure rate of most electronic components can be predicted as λ_p=λ_b×π₁ ×π₂···π_n. A statistical method of obtaining these parameters is presented. The method is based on large quantities of field data, and the more current the data are, the more accurate the values of each level of λ_b and π_i. Using these values, the models in US Mil-Hdbk-217 can be applied to predict the life of electronic products. The method can also be used to verify the present values of each level of λ_b and π_i, and to provide the basis of further amendment     

Order selection statistical test for nonstationary AR models

A new statistical test for selecting the order of a nonstationary AR modelyk is presented based on the predictive least-squares principle. This test is of the same order as the accumulated cost function _n = _k=1 ⁿ ( _k ^* – _k )²;i.e., ^* wherey _k ^* is the predictive least-square estimate. It is constructed to show how many times the integrated AR processy _k is differenced in order to obtain a stationary AR process given that the exact order of the process is unknown.     

Confidence regions for statistical model based shape prediction from sparse observations

Shape prediction from sparse observation is of increasing interest in minimally invasive surgery, in particular when the target is not directly visible on images. This can be caused by a limited field-of-view of the imaging device, missing contrast or an insufficient signal-to-noise ratio. In such situations, a statistical shape model can be employed to estimate the location of unseen parts of the organ of interest from the observation and identification of the visible parts. However, the quantification of the reliability of such a prediction can be crucial for patient safety. We present here a framework for the estimation of complete shapes and of the associated uncertainties. This paper formalizes and extends previous work in the area by taking into account and incorporating the major sources of uncertainties, in particular the estimation of pose together with shape parameters, as well as the identification of correspondences between the sparse observation and the model. We evaluate our methodology on a large database of 171 human femurs and synthetic experiments based on a liver model. The experiments show that informative and reliable confidence regions can be estimated by the proposed approach.     

Comparison of statistical models for the purpose of designing an optimum predictor

A comparison of two different statistical input models is made in order to decide upon which of these models the design of an optimum predictor should be based. Under certain circumstances, samples of typical data may be fitted equally well by two or more statistical input models. Since it is necessary to have a statistical model for the input before an optimal predictor can be designed, a method is developed for deciding which of these models to choose. Two specific models are used in the comparison, but the method is general and may be applied to other models. The method of comparison is to design an optimal predictor based on each input model assumption and determine how well each filter would perform if the other input were applied. The filter with the best performance for either input model assumption is chosen. The performance for each filter is measured by the mean squared error. The results are summarized in curves of mean squared error versus observation time for a given signal filter configuration.     

A set of analytic formulas for capacitance of VLSI interconnects oftrapezium shape

In VLSI timing analysis, a quick and accurate extraction of interconnect line to line and line to ground capacitance is very important. This paper presents a set of analytic formulas for the capacitance of one, two, and three trapezium interconnect lines over a ground plane which show better than 7% agreement with the capacitance values from the numerical simulation     

Quantitative statistical assessment of conditional models for synthetic aperture radar

Many applications of object recognition in the presence of pose uncertainty rely on statistical models-conditioned on pose-for observations. The image statistics of three-dimensional (3-D) objects are often assumed to belong to a family of distributions with unknown model parameters that vary with one or more continuous-valued pose parameters. Many methods for statistical model assessment, for example the tests of Kolmogorov-Smirnov and K. Pearson, require that all model parameters be fully specified or that sample sizes be large. Assessing pose-dependent models from a finite number of observations over a variety of poses can violate these requirements. However, a large number of small samples, corresponding to unique combinations of object, pose, and pixel location, are often available. We develop methods for model testing which assume a large number of small samples and apply them to the comparison of three models for synthetic aperture radar images of 3-D objects with varying pose. Each model is directly related to the Gaussian distribution and is assessed both in terms of goodness-of-fit and underlying model assumptions, such as independence, known mean, and homoscedasticity. Test results are presented in terms of the functional relationship between a given significance level and the percentage of samples that wold fail a test at that level.     

Comparative analysis of statistical models for the simulation of Rayleigh faded cellular channels

Several new "sum-of-sinusoids" models have recently been introduced for the simulation of Rayleigh fading channels. These models are statistical in nature implying that their simulation parameters such as the Doppler frequencies are random. They have been shown to accurately reproduce some of the desired statistical properties of the faded envelope such as the time autocorrelation and the level crossing rate. However, a comparative analysis of these models, hitherto scattered throughout the literature, is not available. This paper compares these models in terms of their complexity and performance. The performance assessment is based upon the variance of the time average statistical properties from their ideal ensemble averages.