Statistical Analysis Of Image Features

图像特征统计分析

• 批准号: 6818455
• 负责人: Daniel W Hommer
• 金额: --
• 依托单位: NATIONAL INSTITUTE ON ALCOHOL ABUSE AND ALCOHOLISM
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6818455
• 关键词: alcoholic beverage consumption; alcoholism /alcohol abuse; bioimaging /biomedical imaging; brain metabolism; cerebrovascular imaging /visualization; clinical research; digital imaging; functional magnetic resonance imaging; glucose metabolism; human subject; image processing; information display; mathematical model; method development; positron emission tomography; statistics /biometry

The aim of this project is the development of statistical methods that either take into account interpixel correlation, or apply global image transform methods that permit an analysis of uncorrelated image components. Of typical interest is the investigation of differences between either images from individual subjects acquired under different experimental conditions, or between average images of subjects from different diagnostic groups. Three different statistical methods have been developed, based on the Fourier transform, the wavelet transform, and the theory of Gaussian random fields in the spatial domain. In the Fourier domain, the statistics at different wave numbers are uncorrelated and inference tests can be performed unencumbered by spatial correlations. This method provides for rigorous statistical tests with well-known properties and interpretations, but results in spatially uniform image blurring and may yield relatively poor spatial localization. For the wavelet-transform based analysis, a mathematically rigorous theory has been established that applies parametric statistical tests on wavelet coefficients and results in estimates of local image differences by inverse wavelet transform of only significant coefficients. The method provides for good spatial localization and the implementation of locally adaptive image smoothing, but there has not been much experience accumulated for the interpretation of test outcomes and estimates of image differences. Gaussian random field analysis has good spatial localization properties and permits the investigation of correlations with external variables (e.g., age), but it results in spatially uniform image blurring and does not provide for statistically reconstructed estimates of images differences (either across group or conditions). All three methods have been applied to the analysis of PET images from normal and alcoholic subjects and have identified significant differences in generally the same brain regions. Gaussian random field analysis was able to demonstrate in PET images from alcoholics a significant negative correlation of glucose utilization in the pre-frontal cortex with age. Current research on these topics includes the development of a 1-D Gaussian random field method to analyze fMRI time series data. This methodology can be used to analyze fMRI data acquired from experiments designed to incorporate a long (that is long enough, as determined experimentally, to estimate the variance associated with the acquired data) baseline condition and transition to another activated state. It uses the long baseline data to estimate the variance measure associated with the temporal data from a voxel within the image and sets a statistically rigorous threshold for activation in spite of the known temporal correlation in the data. This analysis technique is being validated with simulated and experimental data. Furthermore, this analysis technique is being incorporated into numerous experiments including one designed to look at the blood flow changes in the brain associated with alcoholic intake in normal subjects. This presents an ideal demonstration of this analysis technique to basically establish a response curve for alcohol intake. Finally, statistical analysis in the temporal domain based on traditional time series analysis in the Fourier domain have been developed and given similar results in terms of localization of the signal in fMRI blood flow studies to other less rigorous and generalizable techniques. This analysis methodology has the potential to (1) localize fMRI activation changes, (2) estimate or reconstruct the activated signal without the associated noise, (3) estimate the hemodynamic response function locally without prior assumptions as to its structure, and (4) detect multiple responses to multiple input stimuli. Currently this technique is being used to study both simple finger tap data as well as more complex experimental designs including slides of visual stimuli designed to elicit different emotions or alcohol craving.

该项目的目标是开发统计方法，要么考虑像素间相关性，要么应用允许分析不相关图像成分的全局图像变换方法。通常感兴趣的是研究在不同实验条件下获取的个体受试者的图像之间的差异，或来自不同诊断组的受试者的平均图像之间的差异。基于傅里叶变换、小波变换和空间域高斯随机场理论，已经开发了三种不同的统计方法。在傅立叶域中，不同波数的统计量是不相关的，并且可以不受空间相关性的阻碍进行推理测试。该方法提供了具有众所周知的属性和解释的严格统计测试，但会导致空间均匀的图像模糊，并且可能产生相对较差的空间定位。对于基于小波变换的分析，已经建立了数学上严格的理论，该理论对小波系数应用参数统计测试，并通过仅对有效系数进行逆小波变换来估计局部图像差异。该方法提供了良好的空间定位和局部自适应图像平滑的实现，但在测试结果的解释和图像差异的估计方面尚未积累太多经验。高斯随机场分析具有良好的空间定位特性，并允许研究与外部变量（例如年龄）的相关性，但它会导致空间均匀的图像模糊，并且不提供图像差异的统计重建估计（跨组或条件） 。所有三种方法均已应用于分析正常人和酗酒者的 PET 图像，并发现了大体相同的大脑区域的显着差异。高斯随机场分析能够在酗酒者的 PET 图像中证明，前额皮质中的葡萄糖利用率与年龄呈显着负相关。目前关于这些主题的研究包括开发一维高斯随机场方法来分析功能磁共振成像时间序列数据。该方法可用于分析从实验中获取的功能磁共振成像数据，该实验旨在合并长基线条件（根据实验确定，足够长，以估计与获取的数据相关的方差）基线条件并过渡到另一个激活状态。它使用长基线数据来估计与图像内体素的时间数据相关的方差度量，并设置统计上严格的激活阈值，尽管数据中存在已知的时间相关性。该分析技术正在通过模拟和实验数据进行验证。此外，这种分析技术正在被纳入众多实验中，其中包括一项旨在观察正常受试者与酒精摄入相关的大脑血流变化的实验。这完美地展示了这种分析技术，可以基本建立酒精摄入量的响应曲线。最后，基于傅里叶域中传统时间序列分析的时域统计分析已经得到发展，并且在功能磁共振成像血流研究中的信号定位方面给出了与其他不太严格和通用的技术类似的结果。这种分析方法有可能 (1) 定位 fMRI 激活变化，(2) 估计或重建激活信号，而无需相关噪声，(3) 局部估计血流动力学响应函数，而无需事先假设其结构，(4)检测对多种输入刺激的多种反应。目前，该技术被用于研究简单的手指敲击数据以及更复杂的实验设计，包括旨在引发不同情绪或酒精渴望的视觉刺激幻灯片。