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Automated craniofacial landmarks detection on 3D image using geometry characteristics information

Background: Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. C...

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Main Authors: Abu, Arpah, Ngo, Chee Guan, Abu-Hassan, Nur Idayu Adira, Othman, Siti Adibah
Format: Article
Language:English
Published: BioMed Central 2019
Subjects:
RK Dentistry
Practice of dentistry. Dental economics
Online Access:http://eprints.um.edu.my/23879/1/s12859-018-2548-9
http://eprints.um.edu.my/23879/
https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2548-9
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spelling my.um.eprints.238792020-02-21T01:56:46Z http://eprints.um.edu.my/23879/ Automated craniofacial landmarks detection on 3D image using geometry characteristics information Abu, Arpah Ngo, Chee Guan Abu-Hassan, Nur Idayu Adira Othman, Siti Adibah RK Dentistry Practice of dentistry. Dental economics Background: Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. Currently, most anthropometric studies use landmark points that are manually plotted on a 3D facial image by the examiner. This method is time-consuming and leads to human biases, which will vary from intra-examiners to inter-examiners when involving large data sets. Biased judgment also leads to a wider gap in measurement error. Thus, this work aims to automate the process of landmarks detection to help in enhancing the accuracy of measurement. In this work, automated craniofacial landmarks (ACL) on a 3D facial image system was developed using geometry characteristics information to identify the nasion (n), pronasale (prn), subnasale (sn), alare (al), labiale superius (ls), stomion (sto), labiale inferius (li), and chelion (ch). These landmarks were detected on the 3D facial image in .obj file format. The IA was also performed by manually plotting the craniofacial landmarks using Mirror software. In both methods, once all landmarks were detected, the eight linear measurements were then extracted. Paired t-test was performed to check the validity of ACL (i) between the subjects and (ii) between the two methods, by comparing the linear measurements extracted from both ACL and AI. The tests were performed on 60 subjects (30 males and 30 females). Results: The results on the validity of the ACL against IA between the subjects show accurate detection of n, sn, prn, sto, ls and li landmarks. The paired t-test showed that the seven linear measurements were statistically significant when p < 0.05. As for the results on the validity of the ACL against IA between the methods, ACL is more accurate when p ≈ 0.03. Conclusions: In conclusion, ACL has been validated with the eight landmarks and is suitable for automated facial recognition. ACL has proved its validity and demonstrated the practicability to be used as an alternative for IA, as it is time-saving and free from human biases. BioMed Central 2019 Article PeerReviewed text en http://eprints.um.edu.my/23879/1/s12859-018-2548-9 Abu, Arpah and Ngo, Chee Guan and Abu-Hassan, Nur Idayu Adira and Othman, Siti Adibah (2019) Automated craniofacial landmarks detection on 3D image using geometry characteristics information. BMC Bioinformatics, 19 (Sup.13). pp. 65-80. ISSN 1471-2105 https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2548-9 doi:10.1186/s12859-018-2548-9
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic RK Dentistry
Practice of dentistry. Dental economics
spellingShingle RK Dentistry
Practice of dentistry. Dental economics
Abu, Arpah
Ngo, Chee Guan
Abu-Hassan, Nur Idayu Adira
Othman, Siti Adibah
Automated craniofacial landmarks detection on 3D image using geometry characteristics information
description Background: Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. Currently, most anthropometric studies use landmark points that are manually plotted on a 3D facial image by the examiner. This method is time-consuming and leads to human biases, which will vary from intra-examiners to inter-examiners when involving large data sets. Biased judgment also leads to a wider gap in measurement error. Thus, this work aims to automate the process of landmarks detection to help in enhancing the accuracy of measurement. In this work, automated craniofacial landmarks (ACL) on a 3D facial image system was developed using geometry characteristics information to identify the nasion (n), pronasale (prn), subnasale (sn), alare (al), labiale superius (ls), stomion (sto), labiale inferius (li), and chelion (ch). These landmarks were detected on the 3D facial image in .obj file format. The IA was also performed by manually plotting the craniofacial landmarks using Mirror software. In both methods, once all landmarks were detected, the eight linear measurements were then extracted. Paired t-test was performed to check the validity of ACL (i) between the subjects and (ii) between the two methods, by comparing the linear measurements extracted from both ACL and AI. The tests were performed on 60 subjects (30 males and 30 females). Results: The results on the validity of the ACL against IA between the subjects show accurate detection of n, sn, prn, sto, ls and li landmarks. The paired t-test showed that the seven linear measurements were statistically significant when p < 0.05. As for the results on the validity of the ACL against IA between the methods, ACL is more accurate when p ≈ 0.03. Conclusions: In conclusion, ACL has been validated with the eight landmarks and is suitable for automated facial recognition. ACL has proved its validity and demonstrated the practicability to be used as an alternative for IA, as it is time-saving and free from human biases.
format Article
author Abu, Arpah
Ngo, Chee Guan
Abu-Hassan, Nur Idayu Adira
Othman, Siti Adibah
author_facet Abu, Arpah
Ngo, Chee Guan
Abu-Hassan, Nur Idayu Adira
Othman, Siti Adibah
author_sort Abu, Arpah
title Automated craniofacial landmarks detection on 3D image using geometry characteristics information
title_short Automated craniofacial landmarks detection on 3D image using geometry characteristics information
title_full Automated craniofacial landmarks detection on 3D image using geometry characteristics information
title_fullStr Automated craniofacial landmarks detection on 3D image using geometry characteristics information
title_full_unstemmed Automated craniofacial landmarks detection on 3D image using geometry characteristics information
title_sort automated craniofacial landmarks detection on 3d image using geometry characteristics information
publisher BioMed Central
publishDate 2019
url http://eprints.um.edu.my/23879/1/s12859-018-2548-9
http://eprints.um.edu.my/23879/
https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2548-9
_version_ 1662755191995236352
score 13.211869

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