Dpt. Signal Theory, Networking and Communications, University of Granada, Spain.
Neuroimage. 2013 Jan 15;65:449-55. doi: 10.1016/j.neuroimage.2012.10.005. Epub 2012 Oct 11.
In this work, a linear procedure to perform the intensity normalization of FP-CIT SPECT brain images is presented. This proposed methodology is based on the fact that the histogram of intensity values can be fitted accurately using a positive skewed α-stable distribution. Then, the predicted α-stable parameters and the location-scale property are used to linearly transform the intensity values in each voxel. This transformation is performed such that the new histograms in each image have a pre-specified α-stable distribution with desired location and dispersion values. The proposed methodology is compared with a similar approach assuming Gaussian distribution and the widely used specific-to-nonspecific ratio. In this work, we show that the linear normalization method using the α-stable distribution outperforms those existing methods.
本工作提出了一种线性程序，用于对 FP-CIT SPECT 脑图像进行强度归一化。该方法基于这样一个事实，即强度值的直方图可以用一个正偏 α-稳定分布来准确拟合。然后，使用预测的 α-稳定参数和位置-尺度属性在线性变换每个体素的强度值。这种变换是这样进行的，使得每个图像中的新直方图具有指定的 α-稳定分布，具有所需的位置和分散值。所提出的方法与假设高斯分布和广泛使用的特异性与非特异性比的类似方法进行了比较。在这项工作中，我们表明，使用 α-稳定分布的线性归一化方法优于现有的方法。
