An improved analysis of experimental data from 125 I hot-line autoradiographs: allowing for the effects of background grains

In this paper we suggest a new statistical method of correcting the results of hot-line experiments for the effects of background sources and we use the new method to reassess the adequacy of three probability distributions proposed in the literature for image spread from line sources. The data are from sources labelled with ¹²⁵I in semi-thin resin sections 0·4-0·8 μm in thickness. The new method reveals that two of the models describe the empirical distributions more closely than earlier analysis had suggested, and it confirms an increasing relationship between half distance of image spread and the thickness of the source. However, it also confirms that considerable ‘inter hot-line’ experimental variation remains, even after background correction. This suggests that multiple experiments are needed to produce reliable estimates of half distance.     

Neighbourhood structure estimation of images using hierarchicaltesting

Methods for estimating model parameters using likelihood techniques are examined and a model selection procedure is proposed for classifying the neighbourhood structure of the image. The techniques are investigated using simulated data     

Bayesian estimation for homogeneous and inhomogeneous Gaussianrandom fields

This paper investigates Bayesian estimation for Gaussian Markov random fields. In particular, a new class of compound model is proposed which describes the observed intensities using an inhomogeneous model and the degree of spatial variation described by a second random field. The coupled Markov random fields are used as prior distributions, and combined with Gaussian noise models to produce posterior distributions on which estimation is based. All model parameters are estimated, in a fully Bayesian setting, using the Metropolis-Hasting algorithm. The full posterior estimation procedures are illustrated and compared using various artificial examples. For these examples the inhomogeneous model performs very favorably when compared to the homogeneous model, allowing differential degrees of smoothing and varying local textures