Abstract
We examine alternative 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 based on dynamic time warping (DTW). Our goal is to minimise 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 improves performance of all outlining algorithms, that the contouring algorithm used with the 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.
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Davis, L., Theobald, BJ., Toms, A., Bagnall, A. (2011). On the Extraction and Classification of Hand Outlines. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_12
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DOI: https://doi.org/10.1007/978-3-642-23878-9_12
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