Affiliations 

  • 1 Department of Computer Science, Faculty of Computer Science and IT, Universiti Putra Malaysia, Serdang, Selangor Darul Ehsan, Malaysia
  • 2 Department of Computer Science, Faculty of Computer Science and IT, Universiti Putra Malaysia, Serdang, Selangor Darul Ehsan, Malaysia. azree@upm.edu.my
  • 3 Department of Software Engineering, Faculty of Computer Science and IT, Universiti Putra Malaysia, Serdang, Selangor Darul Ehsan, Malaysia
  • 4 Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor Darul Ehsan, Malaysia
Sci Rep, 2021 10 21;11(1):20767.
PMID: 34675349 DOI: 10.1038/s41598-021-99944-z

Abstract

Angelman syndrome (AS) is one of the common genetic disorders that could emerge either from a 15q11-q13 deletion or paternal uniparental disomy (UPD) or imprinting or UBE3A mutations. AS comes with various behavioral and phenotypic variability, but the acquisition of subjects for experiment and automating the landmarking process to characterize facial morphology for Angelman syndrome variation investigation are common challenges. By automatically detecting and annotating subject faces, we collected 83 landmarks and 10 anthropometric linear distances were measured from 17 selected anatomical landmarks to account for shape variability. Statistical analyses were performed on the extracted data to investigate facial variation in each age group. There is a correspondence in the results achieved by relative warp (RW) of the principal component (PC) and the thin-plate spline (TPS) interpolation. The group is highly discriminated and the pattern of shape variability is higher in children than other groups when judged by the anthropometric measurement and principal component.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.