Investigation of texture quantification parameters for neurological PET image analysis

We investigate the correlation between the clinical severity of neurodegenerative disease and texture metrics (such as Haralick features) computed using PET images of the brain. Specifically, we explore how the parameters of feature computation - such as the region of interest definition method, and the direction and distance used for texture quantification - affect the correlation between texture-based image metrics and clinical disease severity. The analysis was based on an ongoing Parkinson's disease imaging study, with co-registered PET and MRI images, and tracer predominantly concentrated in the striatum. Disease duration was used as the primary clinical metric. It was found that the region of interest placement method substantially affected the correlation values. Significant correlation (p<0.01) was obtained when simple box-like regions were used instead of the anatomic MRI-based regions. The used direction affected the correlation values moderately, and distance did not have a pronounced effect. The results suggest that the Haralick features and other texture metrics that do not require kinetic modeling could be potentially used for the analysis of PET images for which the corresponding MRI data are not available. The results also show that the region of interest definition method and the direction along which metrics are computed may affect metric performance.