On the segmentation and classification of hand radiographs

Davis, Luke M., Theobald, Barry-John, Lines, Jason ORCID: https://orcid.org/0000-0002-1496-5941, Toms, Andoni and Bagnall, Anthony (2012) On the segmentation and classification of hand radiographs. International Journal of Neural Systems, 22 (5). pp. 1250020-1250036. ISSN 0129-0657

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This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components.

Item Type: Article
Uncontrolled Keywords: age determination by skeleton,aging,algorithms,artificial intelligence,automation,child,female,fingers,fourier analysis,hand,hand bones,humans,image processing, computer-assisted,likelihood functions,male,principal component analysis,reference standards,software,x-rays
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Science > Research Groups > Interactive Graphics and Audio
Faculty of Science > Research Groups > Smart Emerging Technologies
Faculty of Science > Research Groups > Data Science and Statistics
Depositing User: Users 2731 not found.
Date Deposited: 26 Nov 2012 15:57
Last Modified: 21 Apr 2023 23:39
URI: https://ueaeprints.uea.ac.uk/id/eprint/40200
DOI: 10.1142/S0129065712500207

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