Abstract:
Positron Emission Tomography (PET) is an important nu-
clear medicine imaging technique which enhances the e®ectiveness of
diagnosing many diseases. The raw-projection data, i.e. the sinogram,
from which the PET is reconstructed, contains a very high level of Pois-
son noise. The latter complicates the PET image's interpretation which
may lead to erroneous diagnoses. Suitable denoising techniques prior to
reconstruction can signi¯cantly alleviate the problem. In this paper, we
propose ¯ltering the sinogram with a constraint curvature motion di®u-
sion for which we compute the edge stopping function in terms of edge
probability under the assumption of contamination by Poison noise. We
demonstrate through simulations with images contaminated by Poisson
noise that the performance of the proposed method substantially sur-
passes that of recently published methods, both visually and in terms of
statistical measures.
