Abstract:
Radiologists face difficulties when reading
and interpreting Positron Emission Tomography
(PET) images because of the high noise level in the
raw-projection data (i.e. the sinogram). The later
may lead to erroneous diagnoses. Aiming at finding
a suitable denoising technique for PET images, in
our first work, we investigated filtering the sinogram
with a constraint curvature motion filter where we
computed the edge stopping function in terms of
edge probability under a marginal prior on the
noise free gradient. In this paper, we show that
the Chi-square is the appropriate prior for finding
the edge probability in the sinogram noise-free
gradient. Since the sinogram noise is uncorrelated
and follows a Poisson distribution, we then propose
an adaptive probabilistic diffusivity function where
the edge probability is computed at each pixel. We
demonstrate quantitatively and qualitatively through
simulations that the performance of the proposed
method substantially surpasses that of state-of-art
methods, both visually and in terms of statistical
measures.
