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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yang Chen Qianjin Feng Pengcheng Shi Wufan Chen |
| Copyright Year | 2007 |
| Description | Author affiliation: Sch. of Biomed. Eng., Southern Med. Univ., Guangzhou (Yang Chen; Qianjin Feng; Pengcheng Shi; Wufan Chen) |
| Abstract | Bayesian approaches, or maximum a posteriori (MAP) methods, have been accepted as an effective solution to overcome the ill-posed problem of such image reconstructions as positron emission tomography (PET) image reconstruction. Based on Bayesian theory, prior information of the objective image is imposed on image reconstruction to suppress noise. Generally, the information in most of prior models is from a simply weighted differences between the pixel densities in a small local neighborhood, so it can only provide limit prior information for reconstruction. In this paper, a novel nonlocal Markov random fields (MRF) prior, which is able to exploit global information in image using large neighborhoods and a new weighting method, is proposed. Relevant experiments about the proposed prior's application in PET are illustrated. Results and comparisons with other priors proved the proposed nonlocal prior's good performance in both lowering noise effect and preserving edges |
| Starting Page | 149 |
| Ending Page | 152 |
| File Size | 296397 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424406714 |
| DOI | 10.1109/ISBI.2007.356810 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Positron emission tomography Image reconstruction Bayesian methods Pixel Biomedical imaging Markov random fields Biomedical measurements Noise measurement Biomedical engineering Data analysis |
| Content Type | Text |
| Resource Type | Article |
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