DocumentCode
1772099
Title
Fast automatic myopic deconvolution of angiogram sequences
Author
Thibon, Louis ; Soulez, Ferreol ; Thiebaut, Eric
Author_Institution
Centre de Rech. Astrophys. de Lyon, Univ. Lyon 1, Lyon, France
fYear
2014
fDate
April 29 2014-May 2 2014
Firstpage
1067
Lastpage
1070
Abstract
We present a fast unsupervised myopic deconvolution method dedicated to quasi-real time processing of video sequences such as angiograms. Our method is based on a Bayesian approach of which the tuning parameters are automatically set thanks to the marginalized likelihood of the observed image. We demonstrate the effectiveness of our approach on simulated and empirical images.
Keywords
Bayes methods; diagnostic radiography; image sequences; maximum likelihood sequence estimation; medical image processing; Bayesian approach; angiogram sequences; empirical images; fast automatic myopic deconvolution method; image simulation; marginalized likelihood; quasi-real time processing; tuning parameters; video sequences; Approximation methods; Deconvolution; Discrete Fourier transforms; Image restoration; Maximum likelihood estimation; Noise; Tuning;
fLanguage
English
Publisher
ieee
Conference_Titel
Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on
Conference_Location
Beijing
Type
conf
DOI
10.1109/ISBI.2014.6868058
Filename
6868058
Link To Document