Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution

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. ► A linear procedure for intensity normalization using an α-stable distribution. ► α-stable parameters are estimated using the Maximum-Likelihood method. ► Location-scale property is used to linearly transform the voxel intensity values. ► The proposed method is compared with the widely used specific-to-non specific ratio. ► Intersubject differences in the non-specific regions are reduced.