Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes

Bagnall, Anthony and Davis, Luke (2014) Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes.

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Abstract

Bone age assessment is a task performed daily in hospitals worldwide. This involves a clinician estimating the age of a patient from a radiograph of the non-dominant hand. Our approach to automated bone age assessment is to modularise the algorithm into the following three stages: segment and verify hand outline; segment and verify bones; use the bone outlines to construct models of age. In this paper we address the final question: given outlines of bones, can we learn how to predict the bone age of the patient? We examine two alternative approaches. Firstly, we attempt to train classifiers on individual bones to predict the bone stage categories commonly used in bone ageing. Secondly, we construct regression models to directly predict patient age. We demonstrate that models built on summary features of the bone outline perform better than those built using the one dimensional representation of the outline, and also do at least as well as other automated systems. We show that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also demonstrate the utility of the model by quantifying the importance of ethnicity and sex on age development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach for automated bone ageing, since it offers flexibility and transparency and produces accurate estimates

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics
Related URLs:	http://uk.arxiv.org/abs/1406.4781
Depositing User:	Pure Connector
Date Deposited:	25 Jul 2014 15:22
Last Modified:	24 May 2022 15:06
URI:	https://ueaeprints.uea.ac.uk/id/eprint/49612
DOI:

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