BAYESIAN IMAGE-PROCESSING IN MAGNETIC-RESONANCE-IMAGING

被引:13
作者
HU, XP
JOHNSON, V
WONG, WH
CHEN, CT
机构
[1] Department of Radiology, University of Chicago Hospitals, Chicago
[2] Department of Statistics, University of Chicago, Chicago
[3] Institute of Decision and Statistics, Duke University, Durham
基金
美国国家科学基金会;
关键词
MAGNETIC RESONANCE IMAGING; IMAGE PROCESSING; BAYESIAN IMAGE RESTORATION;
D O I
10.1016/0730-725X(91)90049-R
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
In the past several years, image processing techniques based on Bayesian models have received considerable attention. In our earlier work, we developed a novel Bayesian approach which was primarily aimed at the processing and reconstruction of images in positron emission tomography. In this paper, we describe how the technique has been adopted to process magnetic resonance images in order to reduce noise and artifacts, thereby improving image quality. In this framework, the image is assumed to be a statistical variable whose posterior probability density conditional on the observed image is modeled by the product of the likelihood function of the observed data with a prior density based our prior knowledge. A Gibbs random field incorporating local continuity information and with edge-detection capability is used as the prior model. Based on the formalism of the posterior density, we can compute an estimate of the image using an iterative technique. We have implemented this technique and applied it to phantom and clinical images. Our results indicate that the approach works reasonably well for reducing noise, enhancing edges, and removing ringing artifact.
引用
收藏
页码:611 / 620
页数:10
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