Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.661313
Title: Statistical methods for segmenting X-ray CT images of sheep
Author: Robinson, Caroline D.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2000
Availability of Full Text:
Access through EThOS:
Full text unavailable from EThOS. Please try the link below.
Access through Institution:
Abstract:
X-ray computed tomography (CT) is a non-invasive imaging technique widely used in medical diagnosis to detect physiological abnormalities. Recently it has been adopted for estimating tissue proportions in live sheep. This thesis is concerned with the development of statistical methods for automating the estimation of tissue proportions from CT images. The first stage in the estimation process is to segment sectional images into the internal organs, the carcass and the area external to the sheep. This is currently achieved by manually extracting boundaries which encircle the internal organs of the sheep, and is undesirable because it is a very subjective and tedious process. We explore the use of deformable templates to automate this stage, by means of a parametrised stochastic template which describes the shape and variability of these boundaries. The manually segmented boundaries from 24 lumbar images are parametrised using Fourier coefficients, which are reduced in dimensionality using principal components in order to estimate a distribution on the parameters of the template. Templates are fitted to further images using a criterion which combines the local pixel gradient and closeness to the estimated template distribution. Having isolated the carcass region, we estimate the proportions of fat and muscle by modelling the probability density function of the pixel values in the segmented image, taking into account that many pixel values are generated from a mixture of two or more tissues. The spatial response of the CT machine is investigated by examining a sharp boundary in the image. Modelling this response as an isotropic bivariate normal density leads to a new probability density function for the values of the mixed pixels in the image, and hence to a combined distribution with the remaining pixels.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.661313  DOI: Not available
Share: