基于多孔小波融合的PET/CT 图像增强及其评价

目的:为解决融合图像视觉效果增强与量化信息损失之间的矛盾,本文提出一种基于非降采样的多孔小波(àtrous wavelet)分解的PET/CT图像融合方法,使得融合图像既有利于肿瘤诊断又能用于放疗靶区勾画和放射性定量分析。方法:对PET和CT图像分别进行多孔小波分解,以包含肿瘤目标的适当大小的感兴趣区域的清晰度为目标函数,采用Nelder-Mead算法对PET和CT图像高频分解系数之比进行优化获得最终的融合系数,使融合图像充分增加解剖学信息的同时又尽量保持PET图像原有的局部和整体灰度信息。结果:融合图像质量评价表明,本文方法能将有价值的PET功能信息与精确的CT解剖信息结合在一起,并克服传统小波融合损失图像量化信息的不足。结论:基于多孔小波融合的PET/CT图像既能用于肿瘤诊断,又能同时用于肿瘤学放射性计算和适形放疗计划制定等量化研究。     

Torque-related changes in mechanomyographic intensity patterns for the superficial quadriceps femoris muscles

The purpose of this study was to investigate changes in mechanomyographic (MMG) intensity patterns for the vastus lateralis (VL), rectus femoris (RF) and vastus medialis (VM) during submaximal to maximal concentric isokinetic, eccentric isokinetic and isometric muscle actions of the leg extensors. Eleven men (mean ± SD age = 20.1 ± 1.1 years) performed concentric, eccentric and isometric muscle actions of the dominant leg extensors on 3 separate days. Surface MMG signals were detected from the VL, RF and VM, processed with a wavelet analysis and examined with a trend plot. The results indicated that the trend plot was capable of tracking systematic changes in MMG amplitude and frequency with an increase in torque. However, these changes were statistically significant in only 26% of the cases. There were also no consistent differences between muscles or contraction types for the significance of the trend plots.     

Developing indices of temporal dispersion and continuity to map natural vegetation

An accurate and updated natural vegetation map is imperative for sustainable environmental management. This paper proposed a novel natural vegetation mapping algorithm based on time series images. Several indices of temporal dispersion and continuity were established for this purpose: low density (LD), medium density (MD), high density (HD) and medium continuity (MC). These indices were developed based on the particular percentiles-determined section of the EVI2 temporal profiles obtained through continuous wavelet transform. The natural vegetation was generally characterized as with lower temporal dispersion and greater temporal continuity compared with agricultural crops. The proposed methodology incorporated the indices of temporal dispersion and continuity and was applied to 13 provinces in central East China based on 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index with two bands (EVI2) in 2013. An overall accuracy of 92.97% was obtained when compared with 2715 ground truth sites. There was also a good agreement (kappa index = 0.8049) on the distribution and areas of different vegetation types between the MODIS-estimated image and the Landsat 8 OLI interpreted data on two test regions. This study demonstrated the efficiency of the transform and metric integrated time series classification approaches in the fields of land and vegetation cover mapping.     

耳声发射信号的小波分析及应用

为了利用瞬态诱发耳声发射信号,对耳蜗性和蜗后性两种感音神经性耳聋进行诊断和定位,提出了一种基于小波变换的方法,用于对感音神经性耳聋病变进行诊断和定位。首先,获得听力正常耳信号的小波变换的对数均方根－频率曲线范围,及在对侧刺激声作用下,信号受抑制的小波变换的对数均方根－频率曲线范围;然后根据某一受试耳有无对侧刺激声作用下的瞬态诱发耳声发射信号,得出其小波变换的对数均方根曲线及受抑制的对数均方根曲线。最后,检测受试耳的两条曲线是否分别在正常值范围内,由此对受试耳的耳蜗及内侧橄榄耳蜗系统功能进行检测,同时根据每一条曲线将蜗性及蜗后病变定位到某一细致的频带。此方法与目前临床上采用的耳声发射结合ABR和纯音测听的诊断方法能很好地吻合。     

Double‐wavelet transform for multisubject task‐induced functional magnetic resonance imaging data

The goal of this article is to model multisubject task‐induced functional magnetic resonance imaging (fMRI) response among predefined regions of interest (ROIs) of the human brain. Conventional approaches to fMRI analysis only take into account temporal correlations, but do not rigorously model the underlying spatial correlation due to the complexity of estimating and inverting the high dimensional spatio‐temporal covariance matrix. Other spatio‐temporal model approaches estimate the covariance matrix with the assumption of stationary time series, which is not always feasible. To address these limitations, we propose a double‐wavelet approach for modeling the spatio‐temporal brain process. Working with wavelet coefficients simplifies temporal and spatial covariance structure because under regularity conditions, wavelet coefficients are approximately uncorrelated. Different wavelet functions were used to capture different correlation structures in the spatio‐temporal model. The main advantages of the wavelet approach are that it is scalable and that it deals with nonstationarity in brain signals. Simulation studies showed that our method could reduce false‐positive and false‐negative rates by taking into account spatial and temporal correlations simultaneously. We also applied our method to fMRI data to study activation in prespecified ROIs in the prefontal cortex. Data analysis showed that the result using the double‐wavelet approach was more consistent than the conventional approach when sample size decreased.     

小波分析方法在心叶驼绒藜(Ceratoides ewersmanniana)空间格局尺度推绎研究中的应用

以古尔班通古特沙漠南缘莫索湾沙地选取相隔15km的两个200m×200m样地,以建群种心叶驼绒藜(Ceratoides ewersmanniana)及其生境地形为研究对象,应用小波分析定量研究了多尺度上空间格局的推绎以及空间异质性、空间格局依赖于尺度的变化关系。研究发现:小波分析尺度由1(5m)变化到4(20m)时,两个样地小尺度上的异质性和格局被合并到更大的尺度上,当小波分析的尺度大于等于5(25m)时,两个样地的格局变化平稳,对应地形(丘顶、丘坡、丘底)的基频稳定在110m左右,心叶驼绒藜的数量动态变化周期稳定在115～125m之间。结果表明:小波分析对信号整体特征的提取作用实现了小尺度上的信息到大尺度上的聚合。结合小波分析对信号突变点的检测,利用位置方差检验局部空间异质性程度,发现位置方差将大尺度上的格局分解到每个取样小样方,位置方差最大的地点对应的异质性也最强,实现了大尺度上的信息到小尺度上的分解。总结认为应用小波分析可以实现对空间格局的尺度推绎,具有对植被、环境的分布格局以及异质性有双重度量作用,由小波系数以及由其衍生的小波方差、位置方差来实现这种度量,图形表现直观,优越性明显。     

基于小波变换的医学图像编码方法

高分辨率的医学图像具有很大的信息量,影响了整个数字化的远程医疗系统的实时性,因此必须在保证不丢失关键诊断信息的前提下,对医学图像进行必要的压缩。本文提出了在给定小波基下,基于二维小波分解和重构的快速压缩方法。该方法使用了向量量化技术并采用LBG算法设计码本。实验结果证明,采用该方法可获得较高的压缩比和符合诊断要求的压缩图像。     

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcrafi Unmanned Aerial Vehicle （RUAV） sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.     

小波变换与生物医学信号处理

作为数字信号处理领域的一个重要分支,生物医学信号处理理论与技术的研究一直受到国内外科技工作者的高度重视。小波变换是近年来发展起来的一种新的信号分析工具。本文结合生物医学信号与小波变换的特点,探讨了小波变换在生物医学信号处理领域的应用前景。     

基于小波分析的大豆叶绿素a含量高光谱反演模型

 2003和2004年分别在长春市良种场和中国科学院海伦黑土生态实验站实测了大田耕作与水肥耦合作用下大豆（Glycine max）冠层高光谱反射率 与叶绿素a含量数据,对光谱反射率、微分光谱与叶绿素a含量进行了相关分析;采用归一化植被指数(Normalized diffe rence vegetation index, NDVI)、土壤调和植被指数(Soil-adjusted vegetation index, SAVI)、再归一植被指数(Renormalized difference vegetation index, RDVI)、第二修正比值植被指数(Modified second ratio index, MSRI)等建立了大豆叶绿素a反演模型;应用小波分析对采集的光谱反 射率数据进行了能量系数提取,并以小波能量系数作为自变量进行了单变量与多变量回归分析,对大豆叶绿素a进行了估算。研究结果表明,大 豆叶绿素a 与可见光光谱反射率相关性较好,并在红光波段取得最大值(R²>0.70),但在红边处,微分光谱与大豆叶绿素a的相关性较反射率好 得多,在其它波段则相反;由NDVI、SAVI、RDVI、MSRI等植被指数建立的估算模型可以提高大豆叶绿素a的估算精度（R²>0.75）;小波能量系 数回归模型可以进一步提高大豆叶绿素a含量的估算水平,以一个特定小波能量系数作为自变量的回归模型,大豆叶绿素a回归决定系数R²高达 0.78;多变量回归分析结果表明,大豆叶绿素a实测值与预测值的线性回归决定系数R²均高达0.85。以上结果表明, 小波分析可以对高光谱进 行特征变量提取,并可在一定程度上提高大豆生理参数反演精度。