NDLI: Hybrid prior method for positron emission tomographic image reconstruction

Content Provider	IEEE Xplore Digital Library
Author	Mondal, P.P. Rajan, K.
Copyright Year	2004
Description	Author affiliation: Dept. of Phys., Indian Inst. of Sci., Bangalore, India (Mondal, P.P.; Rajan, K.)
Abstract	Maximum a-posteriori (MAP) estimation has the advantage of incorporating prior knowledge in the image reconstruction procedure which makes it a superior estimation technique compared to convolution back-projection (CBP), maximum likelihood (ML) etc. The inclusion of prior knowledge greatly improves the image quality. However excess smoothening occurs as the MAP-iterations are continued. In biomedical imaging sharp reconstruction is of potential use. To meet these requirements a new prior is proposed which is capable of enhancing the edges by recognizing the correlated neighbors while restoring homogeneity in the uniform regions of the reconstruction. The proposed prior serves as a post-processing technique in Bayesian domain, once an approximate smooth reconstruction is generated by MAP-algorithm. Simulated experiments show improved sharp reconstruction with the proposed post-processing technique.
Sponsorship	Faculty of Eng., Univ. of Melbourne IEEE Madras Section, Chennai
Starting Page	73
Ending Page	76
File Size	358306
Page Count	4
File Format	PDF
ISBN	0780382439
DOI	10.1109/ICISIP.2004.1287627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum a posteriori estimation Image quality Maximum likelihood estimation Convolution Bayesian methods Tomography Radioactive decay Image restoration Image reconstruction Biomedical imaging
Content Type	Text
Resource Type	Article

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