Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression

Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it...

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Main Authors: Somkantha,K., Theera-Umpon,N., Auephanwiriyakul,S.
Format: Article
Published: Springer New York 2015
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http://cmuir.cmu.ac.th/handle/6653943832/38987
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-389872015-06-16T08:01:01Z Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression Somkantha,K. Theera-Umpon,N. Auephanwiriyakul,S. Computer Science Applications Radiology, Nuclear Medicine and Imaging Radiological and Ultrasound Technology Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it to assess bone age in young children. Our proposed technique can detect the boundaries of carpal bones in X-ray images by using the information from the vector image model and the edge map. Feature analysis of the carpal bones can reveal the important information for bone age assessment. Five features for bone age assessment are calculated from the boundary extraction result of each carpal bone. All features are taken as input into the support vector regression (SVR) that assesses the bone age. We compare the SVR with the neural network regression (NNR). We use 180 images of carpal bone from a digital hand atlas to assess the bone age of young children from 0 to 6 years old. Leave-one-out cross validation is used for testing the efficiency of the techniques. The opinions of the skilled radiologists provided in the atlas are used as the ground truth in bone age assessment. The SVR is able to provide more accurate bone age assessment results than the NNR. The experimental results from SVR are very close to the bone age assessment by skilled radiologists. © Society for Imaging Informatics in Medicine 2011. 2015-06-16T08:01:01Z 2015-06-16T08:01:01Z 2011-12-01 Article 08971889 2-s2.0-84855575746 10.1007/s10278-011-9372-3 21347746 http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84855575746&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38987 Springer New York
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science Applications
Radiology, Nuclear Medicine and Imaging
Radiological and Ultrasound Technology
spellingShingle Computer Science Applications
Radiology, Nuclear Medicine and Imaging
Radiological and Ultrasound Technology
Somkantha,K.
Theera-Umpon,N.
Auephanwiriyakul,S.
Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression
description Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it to assess bone age in young children. Our proposed technique can detect the boundaries of carpal bones in X-ray images by using the information from the vector image model and the edge map. Feature analysis of the carpal bones can reveal the important information for bone age assessment. Five features for bone age assessment are calculated from the boundary extraction result of each carpal bone. All features are taken as input into the support vector regression (SVR) that assesses the bone age. We compare the SVR with the neural network regression (NNR). We use 180 images of carpal bone from a digital hand atlas to assess the bone age of young children from 0 to 6 years old. Leave-one-out cross validation is used for testing the efficiency of the techniques. The opinions of the skilled radiologists provided in the atlas are used as the ground truth in bone age assessment. The SVR is able to provide more accurate bone age assessment results than the NNR. The experimental results from SVR are very close to the bone age assessment by skilled radiologists. © Society for Imaging Informatics in Medicine 2011.
format Article
author Somkantha,K.
Theera-Umpon,N.
Auephanwiriyakul,S.
author_facet Somkantha,K.
Theera-Umpon,N.
Auephanwiriyakul,S.
author_sort Somkantha,K.
title Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression
title_short Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression
title_full Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression
title_fullStr Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression
title_full_unstemmed Bone age assessment in young children using automatic carpal bone feature extraction and support vector regression
title_sort bone age assessment in young children using automatic carpal bone feature extraction and support vector regression
publisher Springer New York
publishDate 2015
url http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84855575746&origin=inward
http://cmuir.cmu.ac.th/handle/6653943832/38987
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