3D Head Shape Analysis of Suspected Zika Infected Infants

Xiangyang Ju, Idelmo Rangel Garcia, Leonardo De Freitas Silva, Peter Mossey, Dhelal Al-Rudainy, Ashraf Ayoub, Adriana Marques De Mattos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The babies infected from Zika before they are born are at risk for problems with brain development and microcephaly. 3D head images of 43 Zika cases and 43 controls were collected aiming to extract shape characteristics for diagnosis purposes. Principal component analysis (PCA) has been applied on the vaults and faces of the collected 3D images and the scores on the second principal components of the vaults and faces showed significant differences between the control and Zika groups. The shape variations from -2σ to 2σ illustrated the typical characteristics of microcephaly of the Zika babies. Canonical correlation analysis (CCA) showed a significant correlation in the first CCA variates of face and vault which indicated the potential of 3D facial imaging for Zika surveillance. Further head circumferences and distances from ear to ear were measured from the 3D images and preliminary results showed the adding ear to ear distances for classifying control and Zika children strengthened the abilities of tested classification models.

Original languageEnglish
Title of host publicationProceedings - 2018, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018
EditorsQingli Li, Wei Li, Lipo Wang
PublisherIEEE
Number of pages4
ISBN (Electronic)9781538676042
ISBN (Print)9781538676059
DOIs
Publication statusPublished - 4 Feb 2019
Event11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 - Beijing, China
Duration: 13 Oct 201815 Oct 2018

Conference

Conference11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018
CountryChina
CityBeijing
Period13/10/1815/10/18

Fingerprint

Ear
Head
Microcephaly
Principal component analysis
Brain
Principal Component Analysis
Imaging techniques
Control Groups

Keywords

  • 3D imaging
  • shape analysis
  • Zika

Cite this

Ju, X., Garcia, I. R., De Freitas Silva, L., Mossey, P., Al-Rudainy, D., Ayoub, A., & De Mattos, A. M. (2019). 3D Head Shape Analysis of Suspected Zika Infected Infants. In Q. Li, W. Li, & L. Wang (Eds.), Proceedings - 2018, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018 [8633125] IEEE. https://doi.org/10.1109/CISP-BMEI.2018.8633125
Ju, Xiangyang ; Garcia, Idelmo Rangel ; De Freitas Silva, Leonardo ; Mossey, Peter ; Al-Rudainy, Dhelal ; Ayoub, Ashraf ; De Mattos, Adriana Marques. / 3D Head Shape Analysis of Suspected Zika Infected Infants. Proceedings - 2018, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018. editor / Qingli Li ; Wei Li ; Lipo Wang. IEEE, 2019.
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title = "3D Head Shape Analysis of Suspected Zika Infected Infants",
abstract = "The babies infected from Zika before they are born are at risk for problems with brain development and microcephaly. 3D head images of 43 Zika cases and 43 controls were collected aiming to extract shape characteristics for diagnosis purposes. Principal component analysis (PCA) has been applied on the vaults and faces of the collected 3D images and the scores on the second principal components of the vaults and faces showed significant differences between the control and Zika groups. The shape variations from -2σ to 2σ illustrated the typical characteristics of microcephaly of the Zika babies. Canonical correlation analysis (CCA) showed a significant correlation in the first CCA variates of face and vault which indicated the potential of 3D facial imaging for Zika surveillance. Further head circumferences and distances from ear to ear were measured from the 3D images and preliminary results showed the adding ear to ear distances for classifying control and Zika children strengthened the abilities of tested classification models.",
keywords = "3D imaging, shape analysis, Zika",
author = "Xiangyang Ju and Garcia, {Idelmo Rangel} and {De Freitas Silva}, Leonardo and Peter Mossey and Dhelal Al-Rudainy and Ashraf Ayoub and {De Mattos}, {Adriana Marques}",
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Ju, X, Garcia, IR, De Freitas Silva, L, Mossey, P, Al-Rudainy, D, Ayoub, A & De Mattos, AM 2019, 3D Head Shape Analysis of Suspected Zika Infected Infants. in Q Li, W Li & L Wang (eds), Proceedings - 2018, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018., 8633125, IEEE, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018, Beijing, China, 13/10/18. https://doi.org/10.1109/CISP-BMEI.2018.8633125

3D Head Shape Analysis of Suspected Zika Infected Infants. / Ju, Xiangyang; Garcia, Idelmo Rangel; De Freitas Silva, Leonardo; Mossey, Peter; Al-Rudainy, Dhelal; Ayoub, Ashraf; De Mattos, Adriana Marques.

Proceedings - 2018, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018. ed. / Qingli Li; Wei Li; Lipo Wang. IEEE, 2019. 8633125.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - 3D Head Shape Analysis of Suspected Zika Infected Infants

AU - Ju, Xiangyang

AU - Garcia, Idelmo Rangel

AU - De Freitas Silva, Leonardo

AU - Mossey, Peter

AU - Al-Rudainy, Dhelal

AU - Ayoub, Ashraf

AU - De Mattos, Adriana Marques

PY - 2019/2/4

Y1 - 2019/2/4

N2 - The babies infected from Zika before they are born are at risk for problems with brain development and microcephaly. 3D head images of 43 Zika cases and 43 controls were collected aiming to extract shape characteristics for diagnosis purposes. Principal component analysis (PCA) has been applied on the vaults and faces of the collected 3D images and the scores on the second principal components of the vaults and faces showed significant differences between the control and Zika groups. The shape variations from -2σ to 2σ illustrated the typical characteristics of microcephaly of the Zika babies. Canonical correlation analysis (CCA) showed a significant correlation in the first CCA variates of face and vault which indicated the potential of 3D facial imaging for Zika surveillance. Further head circumferences and distances from ear to ear were measured from the 3D images and preliminary results showed the adding ear to ear distances for classifying control and Zika children strengthened the abilities of tested classification models.

AB - The babies infected from Zika before they are born are at risk for problems with brain development and microcephaly. 3D head images of 43 Zika cases and 43 controls were collected aiming to extract shape characteristics for diagnosis purposes. Principal component analysis (PCA) has been applied on the vaults and faces of the collected 3D images and the scores on the second principal components of the vaults and faces showed significant differences between the control and Zika groups. The shape variations from -2σ to 2σ illustrated the typical characteristics of microcephaly of the Zika babies. Canonical correlation analysis (CCA) showed a significant correlation in the first CCA variates of face and vault which indicated the potential of 3D facial imaging for Zika surveillance. Further head circumferences and distances from ear to ear were measured from the 3D images and preliminary results showed the adding ear to ear distances for classifying control and Zika children strengthened the abilities of tested classification models.

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BT - Proceedings - 2018, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018

A2 - Li, Qingli

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PB - IEEE

ER -

Ju X, Garcia IR, De Freitas Silva L, Mossey P, Al-Rudainy D, Ayoub A et al. 3D Head Shape Analysis of Suspected Zika Infected Infants. In Li Q, Li W, Wang L, editors, Proceedings - 2018, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018. IEEE. 2019. 8633125 https://doi.org/10.1109/CISP-BMEI.2018.8633125