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Skeletal Growth Estimation Using Radiographic Image Processing and Analysis

Skeletal Growth Estimation Using Radiographic Image Processing and Analysis
Skeletal Growth Estimation Using Radiographic Image Processing and Analysis
An automated knowledge-based vision system for skeletal growth estimation in children is reported in this paper. Images were obtained from hand radiographs of 32 male and 25 female children of age 1–16 yr. Phalanx bones were automatically localized and segmented using hierarchical inferences and active shape models, respectively. A number of shape descriptors were obtained from the segmented bone contour to quantify skeletal growth. From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males. This level of accuracy is comparable to that of expert pediatric radiologists, which suggests that the proposed approach has a potential application in pediatric medicine.
ASM segmentation, Bayesian estimation, feature extraction, skeletal growth assessment
292-297
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Sharif, Bayan
d57a4cae-a6f0-4ab3-b2d8-ef594a75857f
Chester, Graeme
cfccbc9a-d9aa-46e7-bd97-d248ca3d3fc1
Owen, John
fc90a051-661a-40c5-85de-2aebc04cfe04
Lee, Richard
31789a3c-31a0-43a9-b8d5-fe2383d0cd8a
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Sharif, Bayan
d57a4cae-a6f0-4ab3-b2d8-ef594a75857f
Chester, Graeme
cfccbc9a-d9aa-46e7-bd97-d248ca3d3fc1
Owen, John
fc90a051-661a-40c5-85de-2aebc04cfe04
Lee, Richard
31789a3c-31a0-43a9-b8d5-fe2383d0cd8a

Mahmoodi, Sasan, Sharif, Bayan, Chester, Graeme, Owen, John and Lee, Richard (2000) Skeletal Growth Estimation Using Radiographic Image Processing and Analysis. IEEE Transactions on Information Technology in Biomedicine, 4 (4), 292-297.

Record type: Article

Abstract

An automated knowledge-based vision system for skeletal growth estimation in children is reported in this paper. Images were obtained from hand radiographs of 32 male and 25 female children of age 1–16 yr. Phalanx bones were automatically localized and segmented using hierarchical inferences and active shape models, respectively. A number of shape descriptors were obtained from the segmented bone contour to quantify skeletal growth. From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males. This level of accuracy is comparable to that of expert pediatric radiologists, which suggests that the proposed approach has a potential application in pediatric medicine.

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Published date: December 2000
Keywords: ASM segmentation, Bayesian estimation, feature extraction, skeletal growth assessment
Organisations: Southampton Wireless Group

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Local EPrints ID: 265872
URI: http://eprints.soton.ac.uk/id/eprint/265872
PURE UUID: cc514186-1c75-41e5-b6be-196e30b96550

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Date deposited: 10 Jun 2008 09:11
Last modified: 24 Sep 2020 16:41

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Contributors

Author: Sasan Mahmoodi
Author: Bayan Sharif
Author: Graeme Chester
Author: John Owen
Author: Richard Lee

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