Abstract
In this work a complete approach for estimation of the spatial resolution for the gamma camera imaging based on the [1] is analyzed considering where the body distance is detected (close or far way). The organ of interest most of the times is not well defined, so in that case it is appropriate to use elliptical camera detection instead of circular. The image reconstruction is presented which allows spatially varying amounts of local smoothing. An inhomogeneous Markov random field (M.r.f.) model is described which allows spatially varying degrees of smoothing in the reconstructions and a re-parameterization is proposed which implicitly introduces a local correlation structure in the smoothing parameters using a modified maximum likelihood estimation (MLE) denoted as one step late (OSL) introduced by [2].
Highlights
Procedures using Bayesian approaches incorporate information regarding the nature of the true image in terms of prior models
The most common choice of prior distribution promotes local smoothness, using random field models defined as Gibbs distributions
When the prior parameters are constant across the image, a homogeneous Markov random field model is defined [10]
Summary
Procedures using Bayesian approaches incorporate information regarding the nature of the true image in terms of prior models. The most common choice of prior distribution promotes local smoothness, using random field models defined as Gibbs distributions. When the prior parameters are constant across the image, a homogeneous Markov random field model is defined [10]. A common choice in image applications is a homogeneous Gibbs distribution. An obvious generalization is to allow separate parameters for each pixel, which defines an inhomogeneous model [21,22,23]. This extension allows variable amounts of spatial smoothing across the image. The procedures represent a substantial advance for general image and spatial analysis
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