Article

Bayesian and non-Bayesian probabilistic models for medical image analysis

Imaging Science and Biomedical Engineering Division, Medical School, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, UK
Image and Vision Computing (Impact Factor: 1.96). 09/2003; DOI: 10.1016/S0262-8856(03)00072-6
Source: DBLP

ABSTRACT Bayesian approaches to data analysis are popular in machine vision, and yet the main advantage of Bayes theory, the ability to incorporate prior knowledge in the form of the prior probabilities, may lead to problems in some quantitative tasks. In this paper we demonstrate examples of Bayesian and non-Bayesian techniques from the area of magnetic resonance image (MRI) analysis. Issues raised by these examples are used to illustrate difficulties in Bayesian methods and to motivate an approach based on frequentist methods. We believe this approach to be more suited to quantitative data analysis, and provide a general theory for the use of these methods in learning (Bayes risk) systems and for data fusion. Proofs are given for the more novel aspects of the theory. We conclude with a discussion of the strengths and weaknesses, and the fundamental suitability, of Bayesian and non-Bayesian approaches for MRI analysis in particular, and for machine vision systems in general.

0 Bookmarks
 · 
67 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 1 Abstract One of the most common,problems in image analysis is the estimation and removal of noise or other artefacts (e.g.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper identifies the important role that covariance estimation has to play in the construction of analysis systems. The problem of co-registration for inter-modality clinical volumes is often solved by maximising the so-called mutual information measure. This paper extends the existing theory in this area and suggests a viable way of constructing co- variances for mutual information approaches by treating this algorithm as a bootstrapped likelihood based approach. We provide both theoretical and practical tests of the validity of this method. In doing so we identify important subtleties in the current use of these measures for coregistration. These issues suggest potential improvements in the way that such measures might be constructed and used.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a new algorithm for the feature-space based segmentation of medical image volumes, based on a unified mathematical framework that incorporates both intensity and local gradient information. The algorithm addresses the problem of partial volume tissue estimation and is capable of using multiple image volumes, and the associated multi-dimensional image gradient, to increase tissue separability. Clustering is performed in the combined intensity and gradient histogram, followed by the use of Bayes theory to generate probability maps showing the most likely tissue volume fractions within each voxel, rather than a classification to a single tissue type. The approach also supports reconstruction of images from the estimates of volumetric voxel contents and the tissue model parameters. Evaluation of the algorithm comprised three stages. First, objective measurements of segmen- tation accuracy, and the increase in accuracy when local gradient information was included in the feature space, were produced using simulated magnetic resonance (MR) images of the normal brain. Second, application to clinical MR data was demonstrated using an exemplar medical problem, the measurement of cerebrospinal fluid (CSF) volume in 70 normal volunteers, through comparison to a "bronze-standard" consisting of previously published measurements. Third, the accuracy of the multi- dimensional approach was demonstrated by assessing the errors on reconstructed images produced from the segmentation result. We conclude that the inclusion of gradient information in the feature space can result in significant improvements in segmentation accuracy compared to the use of intensity information alone.

Full-text (3 Sources)

Download
0 Downloads
Available from