24 March 2016 Classification of voting patterns to improve the generalized Hough transform for epiphyses localization
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Abstract
This paper presents a general framework for object localization in medical (and non-medical) images. In particular, we focus on objects of well-defined shape, like epiphyseal regions in hand-radiographs, which are localized based on a voting framework using the Generalized Hough Transform (GHT). We suggest to combine the GHT voting with a classifier which rates the voting characteristics of the GHT model at individual Hough cells. Specifically, a Random Forest Classifier rates whether the model points, voting for an object position, constitute a regular shape or not, and this measure is combined with the GHT votes. With this technique, we achieve a success rate of 99.4% for localizing 12 epiphyseal regions of interest in 412 hand- radiographs. The mean error is 6.6 pixels on images with a mean resolution of 1185×2006 pixels. Furthermore, we analyze the influence of the radius of the local neighborhood which is considered in analyzing the voting characteristics of a Hough cell.
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Ferdinand Hahmann, Gordon Böer, Eric Gabriel, Thomas M. Deserno, Carsten Meyer, Hauke Schramm, "Classification of voting patterns to improve the generalized Hough transform for epiphyses localization", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978509 (24 March 2016); doi: 10.1117/12.2216173; https://doi.org/10.1117/12.2216173
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