Paper Info
Title
A Preliminary Study to Reduce the Missing Wedge Effect by Using a Noise Robust Mojette Reconstruction
Abstract
Apart from the usual methods based on the Radon theorem, the Mojette transform proposes a specific algorithm called Corner Based Inversion (CBI) to reconstruct an image from its projections. Contrary to other transforms, it offers two interesting properties. First, the acquisition follows discrete image geometry and resolves the well-known irregular sampling problem. Second, it updates projection values during the reconstruction such that the sinogram contains only data for not yet reconstructed pixels. These properties could be a solution to reduce the missing wedge effect in tomography. Unfortunately, the CBI algorithm is noise sensitive and reconstruction from corrupted data fails. In this paper, we first develop and optimize a noise-robust CBI algorithm based on data redundancy and noise modelling in the projections. Afterwards, this algorithm is applied in discrete tomography from a specific Radon acquisition. Reconstructed image results are discussed and applications and perspectives to reduce the missing wedge effect are also developed.
Year
DOI
Venue
2010
10.1109/SITIS.2010.33
SITIS
Keywords
Field
DocType
data redundancy,radon theorem,corrupted data,discrete image geometry,missing wedge effect,specific algorithm,noise-robust cbi algorithm,cbi algorithm,noise robust mojette reconstruction,preliminary study,discrete tomography,reconstructed image result,computational geometry,image reconstruction,noise measurement,spline,noise,pixel
Iterative reconstruction,Computer vision,Noise measurement,Computer science,Discrete tomography,Computational geometry,Tomography,Data redundancy,Artificial intelligence,Pixel,Mojette Transform
Conference
Citations 
PageRank 
References 
1
0.39
7
Authors
3
Name
Order
Citations
PageRank
B. Recur1123.16
P Desbarats272.34
J. -P. Domenger3522.81