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An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

Hodgson, S., Harrison, R.F. and Cross, S.C. (2006) An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Image and Vision Computing, 24 (9). pp. 1025-1038. ISSN 0262-8856

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Abstract

This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists.

Item Type: Article
Copyright, Publisher and Additional Information: © 2006 Elsevier B.V. This is an author produced version of a paper published in Image and Vision Computing. Uploaded in accordance with the publisher's self-archiving policy.
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine (Sheffield) > Division of Genomic Medicine (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 31 Jul 2008 10:47
Last Modified: 08 Feb 2013 16:56
Published Version: http://dx.doi.org/10.1016/j.imavis.2006.02.019
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.imavis.2006.02.019
URI: http://eprints.whiterose.ac.uk/id/eprint/4126

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