Mixture effects in FIR low-pass filtered signals

Accurate classification of signals composed of two or more classification classes (e.g., biomedical imaging data with pathological structures) might utilize a density that takes account of the signal acquisition process. A new density based on a Gaussian point spread function (PSF) and another utilizing a phenomenological observation known as Benford´s Law are presented. Histograms of filtered signals are compared with these densities. The results suggest that the Gaussian PSF-based density is somewhat better than the Benford´s Law density. Both approaches provide improved fits to histograms from data convolved with a variety of different PSFs over an existing mixture formulation.