含有右删失和区间删失数据的生存函数的非参数估计

删失数据的处理在很多研究领域都是常见的问题,尤其是在医学生存数据分析领域。插补方法是处理删失数据重要的方法之一。然而多数插补方法是将删失数据直接插补成精确数据,这样就扭曲了数据的真实分布,降低了估计方法的精度。本文提出一种含有右删失和区间删失数据的非参数估计方法并与经典SC(self-consistent)算法进行比较。该方法基于均值插补法和最近邻插补法将右删失数据插补为区间删失数据,从而大大提高了真实数据落入插补区间的概率,继而根据经验分布理论对区间删失数据进行生存函数估计。模拟数据和真实乳腺癌数据的分析得出:新算法对删失比例不同的删失数据的估计有更高的精确度和更好的稳健性。本文为临床研究治疗方法效果的比较和估计患者的生存数据提供了一种较好的方法,也为医学生存数据分析提供了一定的帮助。     

一种基于GAOF的早期面瘫运动估计方法

面部表情运动估计是面瘫早期诊断和治疗评价等领域即将出现的一种重要的关键技术。本文提出了一种基于遗传算法的光流方法 (OpticalFlowMethodbasedonGeneticAlgorithm ,GAOF)进行早期面瘫运动估计。该方法从面部动态图像序列中估计面部微小运动矢量分布、特征参量及动态模式 ,用于早期面瘫的定位诊断与治疗定量评价。对一组正常被试者与一组面部神经肌肉运动异常病人进行实验 ,结果表明 :该方法与其他诊断方法相结合 ,对于早期面瘫定位诊断和恢复程度定量评价非常有效。与其他方法相比具有定位客观 ,定量检测精度高和便于进行动态模式评价等优点     

用模糊数学方法融合多模医学图像

提出一种基于模糊数学的方法来融合多模医学图像。采用改进的FCM算法分割图像,用自动模糊重分布的算法确定隶属度。在融合步骤中考虑到了16种不同图像组织的交混情况和16种上下文关系,总共256种模糊关系。实验结果表明:该方法有很强的抗配准偏差能力和抗分割干扰能力,并具有稳健、快速、精确等特点。     

神经元参数估计的符号动力学方法

提出一种与现有的参数估计完全不同的方法—用符号动力学实现参数估计。按符号动力学原理对系统输出信号进行粗粒化测量,根据符号序列的距离来衡量两条轨道的接近程度,可实现高精度的参数估计,精度与输出符号序列的长度相关。采用26个脉冲,估计的误差即可小于1/1000。该方法不需要对系统输出作精确测量,对不稳定系统也能实现估计,对于脉冲式工作的神经元的参数估计尤为有效。     

一种新的仿射参数估计方法在磁共振成像PROPELLER中的应用

本文提出了一种基于频域相位相关算法的仿射参数估计方法,以改进PROPELLER仿射伪影消除中现有方法的不足。首先利用频域相位相关算法求出每个k-空间条的刚性运动参数,然后把刚性运动参数作为初值代入到仿射估计中,进行运动补偿后由网格化重建得到最终结果。实验结果证实该方法对于仿射参数估计的精度更高,稳定性更好,仿射运动伪影消除效果明显优于现有方法。该方法在PROPELLER伪影消除中是一种有效的并且实用的仿射参数估计方法。     

基于矩估计的生物序列模体的贝叶斯检验

针对生物序列模体的显著性检验问题,提出了一种基于矩估计的贝叶斯检验方法.将模体的显著性检验转化为多项分布的检验问题,选取Dirichlet分布作为多项分布的先验分布,并采用矩估计方法估计Dirichlet的超参数,最后应用贝叶斯定理得到一个贝叶斯因子,用于评价模体检验的统计显著性,这种方法克服了传统多项分布检验中构造检验统计量并计算其在零假设下确切分布中的困难.选择JASPAR数据库中107个转录因子结合位点和100组随机模拟数据进行实验,采用皮尔逊积矩相关系数作为评价检验质量的一个标准,实验结果优于传统的模体检验的一些方法,例如快速傅里叶方法.     

关于选择脉冲多普勒超声谱的平均频率和带宽估计的耗费/收益标准

关于选择脉冲多普勒超声谱的平均频率和带宽估计的耗费／收益标准［英］ＲｕａｎｏＭＧ…／／ＩＥＥＥＴｒａｎｓＢＭＥ．——本文讨论一种灵活的谱估计选择标准。该标准基于在计算复杂性（耗费）较低的条件下，对决定性的谱参数估计的加权统计准确度（收益）。这种新的耗...     

噪声动态估计下的心音降噪

针对基于小波分析和经验模式分解的降噪方法本质上不能追踪并消除噪声且容易造成心音失真的问题,本文提出了一种融合改进最小值控制递归平均和最优修正对数谱幅度估计的心音降噪方法。该方法采用短时窗平滑动态追踪、估计噪声最小值,并将噪声估计用于获取最优频谱增益函数,通过最小化干净心音与估计的干净心音的差异来最大限度地抑制噪声。此外,结合心音时频图主观分析和对正常与异常心音分类系统贡献性的客观分析,提出了一种更为严格的评价机制。实验结果表明,本文提出的方法有效地改善了心音信号的时频特征,并在正常与异常心音分类系统中获得了更高的评分。本文提出方法能够帮助医务工作者提高听诊的准确性,对计算机辅助诊断系统的构建与应用也具有十分重要的参考价值。     

一种韧性的胃电信号传导速率估计方法

由于棘波和/或动作伪迹等因素的影响,实际测得的胃电信号常常具有脉冲性。根据分数低阶统计量理论,本研究提出一种基于最小平均p范数盲信道辨识的韧性时间延迟估计算法(BCILMP),对胃电信号传导速率进行估计。与最小均方误差时间延迟估计算法(LMSTDE)比较,该算法在高斯和脉冲噪声环境下,均可以较好地估计出时间延迟,计算机仿真验证了其性能。运用该方法对四位胃轻瘫病人的胃电信号进行了传导速率的估计。结果表明:虽然胃轻瘫病人的胃慢波也由胃体大弯侧上1/3处向下端幽门处传播,但是其胃慢波的平均传导速率均比正常人的慢,速率的变化也不都是单调增加的。传导是胃电活动最重要的空间特征之一,时间延迟估计方法可以定量地评价胃慢波传导的速率和模式。     

利用边缘和表面特征实现脑图像CT-MR的自动配准

应用基于CT和MR图像等值特征表面的配准算法对多模医学图像进行了配准研究.在CT、MR图像中提取等值特征表面,进行图像的几何对准,并对结果进行初步评估,同时对该算法的稳健性,搜索最近点策略和插值策略进行了研究.结果表明:这种方法能够达到亚象素级的配准精度,是一种稳健、高精度、全自动的配准方法.     

癫痫脑电的双谱特性研究

双谱分析对于分析处理非高斯、非线性随机信号具有明显优点.脑电信号被认为具有非高斯、非线性的特性.本文对不同发作阶段癫痫患者的脑电信号进行双谱估计,进而研究不同生理条件下脑电的双谱特性.结果表明,不同发作阶段时癫痫脑电信号的高斯偏离程度明显不同,其中双相干系数能够区分不同发作阶段脑电的信号特征,有望成为临床监护和预报癫痫发作的一个指标.     

A robust state-space kinetics-guided framework for dynamic PET image reconstruction

Dynamic PET image reconstruction is a challenging issue due to the low SNR and the large quantity of spatio-temporal data. We propose a robust state-space image reconstruction (SSIR) framework for activity reconstruction in dynamic PET. Unlike statistically-based frame-by-frame methods, tracer kinetic modeling is incorporated to provide physiological guidance for the reconstruction, harnessing the temporal information of the dynamic data. Dynamic reconstruction is formulated in a state-space representation, where a compartmental model describes the kinetic processes in a continuous-time system equation, and the imaging data are expressed in a discrete measurement equation. Tracer activity concentrations are treated as the state variables, and are estimated from the dynamic data. Sampled-data H(∞) filtering is adopted for robust estimation. H(∞) filtering makes no assumptions on the system and measurement statistics, and guarantees bounded estimation error for finite-energy disturbances, leading to robust performance for dynamic data with low SNR and/or errors. This alternative reconstruction approach could help us to deal with unpredictable situations in imaging (e.g. data corruption from failed detector blocks) or inaccurate noise models. Experiments on synthetic phantom and patient PET data are performed to demonstrate feasibility of the SSIR framework, and to explore its potential advantages over frame-by-frame statistical reconstruction approaches.     

Variance in parametric images: direct estimation from parametric projections

Recent work has shown that it is possible to apply linear kinetic models to dynamic projection data in PET in order to calculate parameter projections. These can subsequently be back-projected to form parametric images--maps of parameters of physiological interest. Critical to the application of these maps, to test for significant changes between normal and pathophysiology, is an assessment of the statistical uncertainty. In this context, parametric images also include simple integral images from, e.g., [O-15]-water used to calculate statistical parametric maps (SPMs). This paper revisits the concept of parameter projections and presents a more general formulation of the parameter projection derivation as well as a method to estimate parameter variance in projection space, showing which analysis methods (models) can be used. Using simulated pharmacokinetic image data we show that a method based on an analysis in projection space inherently calculates the mathematically rigorous pixel variance. This results in an estimation which is as accurate as either estimating variance in image space during model fitting, or estimation by comparison across sets of parametric images--as might be done between individuals in a group pharmacokinetic PET study. The method based on projections has, however, a higher computational efficiency, and is also shown to be more precise, as reflected in smooth variance distribution images when compared to the other methods.     

基于分数低阶矩的非高斯噪声中诱发电位提取新方法

诱发电位（EP）信号榆测与分析技术是临床医学诊断神经系统损伤及病变的重要手段之一。传统的EP信号提取与分离方法中，通常认为EP信号中混入的EEG等噪声是高斯分布的。近年来一些研究表明TEEG信号具有一定的非高斯特性。α-稳定分布町以更好地描述实际应用中所遇到的具有显著脉冲特性的EEG噪声。文中简要介绍了稳定分们统计特性，推导了一种适用于EP信号分离提取的新算法。计算机模拟和分析表明，这种算法是一种在分数低阶α稳定分布背景噪声条件下具有良好韧性的EP信号分离提取方法。     

Robust image registration for functional magnetic resonance imaging of the brain

Motion-related artifacts are still a major problem in data analysis of functional magnetic resonance imaging (FMRI) studies of brain activation. However, the traditional image registration algorithm is prone to inaccuracy when there are residual variations owing to counting statistics, partial volume effects or biological variation. In particular, susceptibility artifacts usually result in remarkable signal intensity variance, and they can mislead the estimation of motion parameters. In this study, Two robust estimation algorithms for the registration of FMRI images are described. The first estimation algorithm was based on the Newton method and used Tukey's biweight objective function. The second estimation algorithm was based on the Levenberg-Marquardt technique and used a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. The proposed registration methods consisted of the following steps: fast segmentation of the brain region from noisy background as a preprocessing step; pre-registration of the volume centroids to provide a good initial estimation; and two robust estimation algorithms and a voxel sampling technique to find the affine transformation parameters. The accuracy of the algorithms was within 0.5 mm in translation and within 0.5° in rotation. For the FMRI data sets, the performance of the algorithms was visually compared with the AIR 2.0 software, which is a software for image registration, using colour-coded statistical mapping by the Kolmogorov-Smirov method. Experimental results showed, that the algorithms provided significant improvement in correcting motion-related artifacts and can enhance the detection of real brain activation.     

Estimation of respiration rate from three-dimensional acceleration data based on body sensor network

Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.     

基于自适应动态特征库的P300诱发脑电单次提取算法研究

目的 诱发电位的单次提取技术一直是脑电信息处理领域的难题之一,为进一步提高单次提取算法的时间准确性和特征精度,针对体感诱发脑电数据信噪比低、试次间参数变化大的特点,研究诱发脑电参数单次提取新算法,保留试次间诱发脑电的动态特性,并提高估计准确率.方法 基于小波滤波和多元线性分析技术,加入自适应动态特征库并由此提出的诱发脑电P300参数单次提取新方法.随机选取4组小波滤波(WF)后诱发脑电数据,分别叠加平均后进行主成分分析(PCA)组成特征库.单次提取时,针对每试次数据从特征库中选择与当次诱发脑电信号相关系数最高的成分作为自变量开展多元线性回归分析,由回归分析结构重构出单次诱发电位信号并自动提取潜伏期和幅值等关键特征.结果 与专家判定的基准数值相比,新算法预测的P300成分潜伏期与幅值参数更准确,两者的平均差值分别为(11.16±8.60) ms和(1.40±1.34)μV;与常用的叠加平均法结果亦更为接近,平均差值分别为(23.26±25.76) ms和(2.52±2.50) μV,新算法相比传统多元线性回归分析算法具有显著优势.结论 将动态更新的诱发脑电数据主成分样本库应用于小波滤波与多元线性回归方法,能有效保留单次诱发脑电数据中的动态特征,从而提升参数估计的准确率.     

在68Ga EDTA动态PET分析中用正常区域取样和参数成像实现大脑肿瘤的统计分割研究

为了研究和评价用^68GaEDTA动态PET研究和评价脑肿瘤定量分析的可靠性和灵敏度，我们在本文中提出了估计容积分布（distribution volume:DV)和血脑屏障渗透率（k1)分布的线性参数成像模型。我们还用F统计学方法实现了把肿瘤从正常组织中分割出来的方法，用一个三参数双腔室模型描述用PET测量的数据，用于估计DV（K1/k2)和K1的主要计算公式为：Cpet=(K1 k2Vp)∫0^tCpds-k2∫0^tCpetds VpCp和∫0^tCpetds=(DV Vp)∫0^tCpds-(1/k2)Cpet (Vp/k2)Gp，这里的k2是脑内通过血脑屏障到脑外的渗透率，在参数成像中我们采用了一个可靠和如棒的基于像素的局域线性回归算法用于产生DV和K1图像。同样基于像素自由度为2和k-2的F统计学方法采用的计算公式为：F=（（（k-2)k/(2(k^2-1)))D^2。这里的D^2=(x-μ）′S^-1(x-μ），而μ和S分别表示脑肿瘤对侧正常区域内采集的样品的平均值和协方差，这些样品是按照二维空间{（K1，DV）}采集的，而常数k是取样的总数，在不同水平上阈值α，就可以得到不同置信度下的F统计图像，用这个方法对11个肿瘤病人进行了^68GaEDTA动态PET研究，研究结果表明：所有的DV，K1和F图像的质量都很好。而且用于产生DV，K1和不同置信度下的F图像的算法效率很高，容易实现，本研究方法中研究和发展的方法提供了一个有效的集成多维生理信息的工具，这个方法可以改善对肿瘤诊断和处理的灵敏度和特异性。     

Minimisation of parameter estimation errors in dynamic PET: choice of scanning schedules

We addressed the general problem of finding an optimal scan schedule in positron emission tomography (PET) dynamic studies which minimises the errors in estimating the transfer constants between a set of compartments. As an example, the influence of scan intervals in PET on the accuracy of estimation of the rate constants and vascular component in the deoxyglucose method was examined using an empirical noise model. The simulated noisy curves used in the analysis were compared with patient data to validate the noise model. A series of scan schedules were compared for accuracy of fit by evaluating the determinant of the variance-covariance matrix of the fitted parameters as an index of parameter accuracy. For realistic noise levels there is a monotonic improvement in the index of parameter accuracy with increasing sampling frequency, particularly over the initial minutes after the tracer injection. However, since faster schedules are more susceptible to errors introduced by time mismatches between plasma and tissue curves and impose greater computational and memory overhead, an initial scan duration of 30 s provide a practical trade-off for dynamic PET 18F-fluoro-deoxyglucose studies.     

On superinfection in malaria

The task of proving or disproving that there is superinfection of human beings in malaria has been unresolved since the concept of superinfection was introduced by Macdonald (1950). We present a method of analysis the results of which indicate that there is superinfection in children up to the age of about five years, but not in older human hosts. We use maximum-likelihood estimation in an extension of ideas introduced by Bekessy, Molineaux, & Storey (1976). The estimation is based on a model for the infection of human hosts with variable degree of superinfection. This model contains a parameter S that equals the maximum number of infections in the host. In the absence of superinfection, S = 1, while S = infinity in the unlimited superinfection assumed by Macdonald. The estimation of S in a number of age bands allows us to identify the age bands where superinfection occurs.