A Nonlinear Probabilistic Curvature Motion Filter for Positron Emission Tomography Images

Authors:
Musa Alrefaya;Hichem Sahli;Iris Vanhamel;Dinh Nho Hao
Affiliations:
Dept. Electronics and Informatics ETRO-IRIS, Vrije Universiteit Brussel, Brussels, Belgium B-1050;Dept. Electronics and Informatics ETRO-IRIS, Vrije Universiteit Brussel, Brussels, Belgium B-1050;Dept. Electronics and Informatics ETRO-IRIS, Vrije Universiteit Brussel, Brussels, Belgium B-1050;Dept. Electronics and Informatics ETRO-IRIS, Vrije Universiteit Brussel, Brussels, Belgium B-1050
Venue:
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Year:
2009

Citing 9
Cited 0

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Geometric partial differential equations and image analysis

Geometric partial differential equations and image analysis
Feature-Oriented Image Enhancement with Shock Filters, 1

Feature-Oriented Image Enhancement with Shock Filters, 1
An Algorithm for Total Variation Minimization and Applications

Journal of Mathematical Imaging and Vision
Smooth minimization of non-smooth functions

Mathematical Programming: Series A and B
Bayesian algorithms for PET image reconstruction with mean curvature and Gauss curvature diffusion regularizations

Computers in Biology and Medicine
Combining curvature motion and edge-preserving denoising

SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Positron Emission Tomography (PET) is an important nuclear medicine imaging technique which enhances the effectiveness of diagnosing many diseases. The raw-projection data, i.e. the sinogram, from which the PET is reconstructed, contains a very high level of Poisson noise. The latter complicates the PET image's interpretation which may lead to erroneous diagnoses. Suitable denoising techniques prior to reconstruction can significantly alleviate the problem. In this paper, we propose filtering the sinogram with a constraint curvature motion diffusion 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 surpasses that of recently published methods, both visually and in terms of statistical measures.