Heine John, Fowler Erin, Schabath Matthew B
Cancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.
Sci Rep. 2024 Dec 28;14(1):30686. doi: 10.1038/s41598-024-78299-1.
An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD). The ID wavelet expansion was applied to WN images (n = 1000). Orthogonality conditions were used to parametrically model the variance decomposition, as described in both domains. FD components were investigated with probability density function modeling and summarized measures. SDN images were constructed by multiplying both simulated and clinical mammograms (both with n = 1000) by WN. The variance decomposition for both WN and SDN decreases exponentially as a parametric function of the ID expansion level; expansion image variances for both types of noise were captured similarly in the Fourier plane corresponding with the ID parametric model. The Fourier transform of WN has a uniform power spectrum distributed exponentially; SDN has similar attributes. Fourier inversion of the lag-autocorrelation performed in the FD produced a statistical estimation of the SDN's image factor. These findings are counterintuitive as SDN can be nonstationary in the ID but have stationary attributes in the FD.
一种原型信号相关噪声（SDN）模型是用于分析从不同技术获取的图像或信号的组件。该模型组件可能与平稳正态白噪声（WN）具有共同特性。来自WN图像的测量值被用作在图像域（ID）和傅里叶域（FD）中与SDN进行比较的标准。将ID小波变换应用于WN图像（n = 1000）。如在两个域中所描述的，使用正交性条件对方差分解进行参数建模。通过概率密度函数建模和汇总测量来研究FD分量。通过将模拟乳腺X线照片和临床乳腺X线照片（均为n = 1000）与WN相乘来构建SDN图像。WN和SDN的方差分解均作为ID扩展水平的参数函数呈指数下降；在与ID参数模型相对应的傅里叶平面中，类似地捕获了两种噪声类型的扩展图像方差。WN的傅里叶变换具有呈指数分布的均匀功率谱；SDN具有类似的属性。在FD中执行的滞后自相关的傅里叶反演产生了SDN图像因子的统计估计。这些发现与直觉相反，因为SDN在ID中可能是非平稳的，但在FD中具有平稳属性。
