prikaz prve stranice dokumenta Initial Results of Multilevel Principal Components Analysis of Facial Shape
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Scientific paper - Original scientific paper
Initial Results of Multilevel Principal Components Analysis of Facial Shape
In: Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science. Springer, 2017. pp. 674-685. urn:nbn:hr:184:289568
Farnell, Damian; Galloway, Jennifer; Zhurov, Alexei; Richmond, Stephen; Perttiniemi, Pertti; Katić, Višnja

Institutional repository: Repository of the University of Rijeka, Faculty of Medicine

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Farnell, D., Galloway, J., Zhurov, A., Richmond, S., Perttiniemi, P. & Katić, V. (2017). Initial Results of Multilevel Principal Components Analysis of Facial Shape. Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science (pp. 674-685). Springer. Retrieved from https://urn.nsk.hr/urn:nbn:hr:184:289568

Farnell, Damian, et al. "Initial Results of Multilevel Principal Components Analysis of Facial Shape." Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, Springer, 2017, pp. 674-685. https://urn.nsk.hr/urn:nbn:hr:184:289568

Farnell, Damian, Jennifer Galloway, Alexei Zhurov, Stephen Richmond, Pertti Perttiniemi and Višnja Katić. "Initial Results of Multilevel Principal Components Analysis of Facial Shape." In Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, 674-685. Springer, 2017. Accessed 2024 October 08. https://urn.nsk.hr/urn:nbn:hr:184:289568

Farnell, D., et al. (2017) 'Initial Results of Multilevel Principal Components Analysis of Facial Shape', Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, Springer, pp. 674-685. Available at: https://urn.nsk.hr/urn:nbn:hr:184:289568 (Accessed 08 October 2024)

Farnell D, Galloway J, Zhurov A, Richmond S, Perttiniemi P, Katić V. Initial Results of Multilevel Principal Components Analysis of Facial Shape. Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science. Springer; 2017. Pp. 674-685. [cited 2024 October 08] Available at: https://urn.nsk.hr/urn:nbn:hr:184:289568

D. Farnell, J. Galloway, A. Zhurov, S. Richmond, P. Perttiniemi and V. Katić, "Initial Results of Multilevel Principal Components Analysis of Facial Shape", Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science Springer, 2017. [Online] Available at: https://urn.nsk.hr/urn:nbn:hr:184:289568 [Accessed: 08 October 2024]

Metadata
Title (english)Initial Results of Multilevel Principal Components Analysis of Facial Shape
AuthorDamian Farnell
AuthorJennifer Galloway
AuthorAlexei Zhurov
AuthorStephen Richmond
AuthorPertti Perttiniemi
AuthorVišnja Katić
Author's institutionUniversity of Rijeka
Faculty of Medicine
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Dental Medicine
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract (english)
Traditionally, active shape models (ASMs) do not make a distinction between groups in the subject population and they rely on methods such as (single-level) principal components analysis (PCA). Multilevel principal components analysis (mPCA) allows one to model between- group effects and within-group effects explicitly. Three dimensional (3D) laser scans were taken from 250 subjects (38 Croatian female, 35 Croatian male, 40 English female, 40 English male, 23 Welsh female, 27 Welsh male, 23 Finnish female, and 24 Finnish male) and 21 landmark points were created subsequently for each scan. After Procrustes transformation, eigenvalues from mPCA and from single-level PCA based on these points were examined. mPCA indicated that the first two eigenvalues of largest magnitude related to within-groups components, but that the next eigenvalue of largest magnitude related to between-groups components. Eigenvalues from single-level PCA always had a larger magnitude than either within-group or between-group eigenvectors at equivalent eigenvalue number. An examination of the first mode of variation indicated possible mixing of between-group and within-group effects in single-level PCA. Component scores for mPCA indicated clustering with country and gender for the between-groups components (as expected), but not for the within-group terms (also as expected). Clustering of component scores for single-level PCA was harder to resolve. In conclusion, mPCA is viable method of forming shape models that offers distinct advantages over single-level PCA when groups occur naturally in the subject population.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review
Publication versionPublished version
Publication titleMedical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science
Numberingpp. 674-685
URN:NBNurn:nbn:hr:184:289568
Publication2017
ConferenceTitle: Medical Image Understanding and Analysis
Acronym: MIUA
Location: Edinburgh, United Kingdom
Organizer: Sinapse
Start date: 2017-07-11
End date: 2017-07-13
Assembly type: Poster
Document URLhttp://bib.irb.hr/882165
Type of resourceText
PublisherSpringer
Access conditionsClosed access
Terms of useLicence In copyright icon
Created on2022-02-23 15:14:27