Automated bone age assessment using feature extraction

Luke M. Davis; Barry-John Theobald; Anthony Bagnall

doi:10.1007/978-3-642-32639-4_6

Automated bone age assessment using feature extraction

Luke M. Davis, Barry-John Theobald, Anthony Bagnall

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Citations (Scopus)

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. In this paper, we propose a combination of image processing and feature extraction algorithms to automatically predict the Tanner-Whitehouse bone stage, the assessment standard used in forming bone age estimates.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2012
Publisher	Springer
Pages	43-51
Number of pages	9
Volume	7435
DOIs	https://doi.org/10.1007/978-3-642-32639-4_6
Publication status	Published - 2012

Publication series

Name	Lecture Notes in Computer Sciences
Publisher	Springer
ISSN (Print)	0302-9743

Access to Document

10.1007/978-3-642-32639-4_6

Cite this

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title = "Automated bone age assessment using feature extraction",

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. In this paper, we propose a combination of image processing and feature extraction algorithms to automatically predict the Tanner-Whitehouse bone stage, the assessment standard used in forming bone age estimates.",

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AB - 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. In this paper, we propose a combination of image processing and feature extraction algorithms to automatically predict the Tanner-Whitehouse bone stage, the assessment standard used in forming bone age estimates.

U2 - 10.1007/978-3-642-32639-4_6

DO - 10.1007/978-3-642-32639-4_6

M3 - Conference contribution

VL - 7435

T3 - Lecture Notes in Computer Sciences

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EP - 51

BT - Intelligent Data Engineering and Automated Learning - IDEAL 2012

PB - Springer

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