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3D analysis of facial morphology

3D analysis of facial morphology
3D analysis of facial morphology
Dense surface models can be used to analyze 3D facial morphology by establishing a correspondence of thousands of points across each 3D face image. The models provide dramatic visualizations of 3D face-shape variation with potential for training physicians to recognize the key components of particular syndromes. We demonstrate their use to visualize and recognize shape differences in a collection of 3D face images that includes 280 controls (2 weeks to 56 years of age), 90 individuals with Noonan syndrome (NS) (7 months to 56 years), and 60 individuals with velo-cardio-facial syndrome (VCFS; 3 to 17 years of age). Ten-fold cross-validation testing of discrimination between the three groups was carried out on unseen test examples using five pattern recognition algorithms (nearest mean, C5.0 decision trees, neural networks, logistic regression, and support vector machines). For discriminating between individuals with NS and controls, the best average sensitivity and specificity levels were 92 and 93% for children, 83 and 94% for adults, and 88 and 94% for the children and adults combined. For individuals with VCFS and controls, the best results were 83 and 92%. In a comparison of individuals with NS and individuals with VCFS, a correct identification rate of 95% was achieved for both syndromes.
facial morphology, dense surface models, 3d analysis, dysmorphology, diagnosis, noonan syndrome, velo-cardio-facial syndrome
1552-4825
339-348
Hammond, Peter
29a53465-6b81-44a1-9390-c8a810e148e9
Hutton, Tim J.
8378de75-db29-4324-8829-aef3e37f439e
Allanson, Judith E.
035e63a4-2446-4b00-8a40-70fac13f8471
Campbell, Linda E.
371996b8-6fec-4559-a379-eddd322ab09d
Hennekam, Raoul C.M.
98431fbd-1697-4874-9d9d-09359eb6d437
Holden, Sean
088bcbed-dbb4-4876-b48f-c162eb01477a
Patton, Michael A.
6a9dcfa4-8434-4102-9b2b-6ab4d510a04d
Shaw, Adam
cca8f84a-f3ea-4e47-8c16-d757e7a38e06
Temple, I. Karen
d63e7c66-9fb0-46c8-855d-ee2607e6c226
Trotter, Matthew
cad73a5a-1729-4a70-9210-908c8c800bfc
Murphy, Kieran C.
eae3a60e-aa25-47fc-85b2-aa60f370e14f
Winter, Robin M.
b917c5da-9ec6-4fd4-ac2e-ed475c3675a7
Hammond, Peter
29a53465-6b81-44a1-9390-c8a810e148e9
Hutton, Tim J.
8378de75-db29-4324-8829-aef3e37f439e
Allanson, Judith E.
035e63a4-2446-4b00-8a40-70fac13f8471
Campbell, Linda E.
371996b8-6fec-4559-a379-eddd322ab09d
Hennekam, Raoul C.M.
98431fbd-1697-4874-9d9d-09359eb6d437
Holden, Sean
088bcbed-dbb4-4876-b48f-c162eb01477a
Patton, Michael A.
6a9dcfa4-8434-4102-9b2b-6ab4d510a04d
Shaw, Adam
cca8f84a-f3ea-4e47-8c16-d757e7a38e06
Temple, I. Karen
d63e7c66-9fb0-46c8-855d-ee2607e6c226
Trotter, Matthew
cad73a5a-1729-4a70-9210-908c8c800bfc
Murphy, Kieran C.
eae3a60e-aa25-47fc-85b2-aa60f370e14f
Winter, Robin M.
b917c5da-9ec6-4fd4-ac2e-ed475c3675a7

Hammond, Peter, Hutton, Tim J., Allanson, Judith E., Campbell, Linda E., Hennekam, Raoul C.M., Holden, Sean, Patton, Michael A., Shaw, Adam, Temple, I. Karen, Trotter, Matthew, Murphy, Kieran C. and Winter, Robin M. (2004) 3D analysis of facial morphology. American Journal of Medical Genetics part A, 126A (4), 339-348. (doi:10.1002/ajmg.a.20665).

Record type: Article

Abstract

Dense surface models can be used to analyze 3D facial morphology by establishing a correspondence of thousands of points across each 3D face image. The models provide dramatic visualizations of 3D face-shape variation with potential for training physicians to recognize the key components of particular syndromes. We demonstrate their use to visualize and recognize shape differences in a collection of 3D face images that includes 280 controls (2 weeks to 56 years of age), 90 individuals with Noonan syndrome (NS) (7 months to 56 years), and 60 individuals with velo-cardio-facial syndrome (VCFS; 3 to 17 years of age). Ten-fold cross-validation testing of discrimination between the three groups was carried out on unseen test examples using five pattern recognition algorithms (nearest mean, C5.0 decision trees, neural networks, logistic regression, and support vector machines). For discriminating between individuals with NS and controls, the best average sensitivity and specificity levels were 92 and 93% for children, 83 and 94% for adults, and 88 and 94% for the children and adults combined. For individuals with VCFS and controls, the best results were 83 and 92%. In a comparison of individuals with NS and individuals with VCFS, a correct identification rate of 95% was achieved for both syndromes.

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More information

Published date: 2004
Additional Information: This article contains supplementary material, which may be viewed at the American Journal of Medical Genetics website at http://www.interscience.wiley.com/jpages/0148-7299/suppmat/index.html.
Keywords: facial morphology, dense surface models, 3d analysis, dysmorphology, diagnosis, noonan syndrome, velo-cardio-facial syndrome

Identifiers

Local EPrints ID: 24736
URI: http://eprints.soton.ac.uk/id/eprint/24736
ISSN: 1552-4825
PURE UUID: b99650b5-2a64-4f36-9a0c-706874457e01
ORCID for I. Karen Temple: ORCID iD orcid.org/0000-0002-6045-1781

Catalogue record

Date deposited: 03 Apr 2006
Last modified: 16 Mar 2024 03:03

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Contributors

Author: Peter Hammond
Author: Tim J. Hutton
Author: Judith E. Allanson
Author: Linda E. Campbell
Author: Raoul C.M. Hennekam
Author: Sean Holden
Author: Michael A. Patton
Author: Adam Shaw
Author: I. Karen Temple ORCID iD
Author: Matthew Trotter
Author: Kieran C. Murphy
Author: Robin M. Winter

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