Analisis statistik bentuk untuk pengkelasan data kraniofasial

Statistical shape analysis can be applied in many disciplines including medical field. In this study, the statistical shape analysis is employed for classification of craniofacial data. The craniofacial data of students are obtained using the Konica Minolta Vivid 910 laser scanner to produce the thr...

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Main Author:	Md. Hussin, Mohd. Bakery
Format:	Thesis
Language:	English
Published:	2010
Subjects:	RZ Other systems of medicine
Online Access:	http://eprints.utm.my/id/eprint/16273/7/MohdBakeryHussainMFKSG2010.pdf
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id	my-utm-ep.16273
record_format	uketd_dc
spelling	my-utm-ep.162732017-09-13T01:30:09Z Analisis statistik bentuk untuk pengkelasan data kraniofasial 2010 Md. Hussin, Mohd. Bakery RZ Other systems of medicine Statistical shape analysis can be applied in many disciplines including medical field. In this study, the statistical shape analysis is employed for classification of craniofacial data. The craniofacial data of students are obtained using the Konica Minolta Vivid 910 laser scanner to produce the three dimensional craniofacial image. The three dimensional coordinates of the surface for each landmark are obtained digitally using RapidForm 2004 software. The methods used are Principal Component Analysis (PCA) and Generalized Procrustes Analysis (GPA), to obtain average configuration of facial point transformation and facial point average, respectively. The craniofacial data are divided into sex and age categories by using the statistical classification method. The craniofacial data are classified by three methods: (i) Procrustes examination analysis of 35 students with 18 landmarks each; (ii) Procrustes examination analysis of 76 data sets with 27 landmarks each, and; (iii) Examination analysis of the 35 data sets with 20 distance measurements each. The results showed that the Procrustes values of male data are larger than the female data, and the Procrustes values of those born in 1989 were larger than those born in 1991. Centroid size differences between the sex and age are significant, hence the values of PCA and GPA can be used for classification analysis. The research results produced the average value for craniofacial data based on age and sex. 2010 Thesis http://eprints.utm.my/id/eprint/16273/ http://eprints.utm.my/id/eprint/16273/7/MohdBakeryHussainMFKSG2010.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate Faculty of Geoinformation and Real Estate Akca, M. D. (2003). Generalized Procrustes Analysis and Its Application in Photogrammetry. Swiss Federal Institut of Technology. Bookstein, F. L. (1991). Morphometric Tools for Landmark Data; Geometry and Biology. The University of Michigan: Cambridge University Press. Cook, D., Swayne, D. F. & Buja, A (1996). Interactive and Dynamic Graphics for Data Analysis; with Examples Using R and Ggobi. Department of Statistics, Iowa State University. Craniofacial Anomalies (2007). http://c-anomalies.net/ Distance (2007). http://mathworld.wolfram.com/Distance.html El-Hussuna, Alaa. (2003). Statistical Variation of Three Dimentional Face Models. ITUniversity of Copenhagen: Tesis Sarjana. Hardy, A. (2005). Validation in Unsupervised Symbolic Classification. Present at ASMDA Conference. May 2005. Department of Mathematics, University of Namur, Belgium. Ian L. Dryden & Kanti V. Mardia (1998). Statistical Shape Analysis. University of Leeds, UK: John Wiley & Sons Ltd. Kolar, J. C. & Salter, E. M. (1996). Craniofacial Anthropometry; Practical Measurement of the Head and Face for Clinical, Surgical and Research Use. Springfield, USA: Charles C. Thomas Publisher Ltd. Lele, S. R. & Richtsmeier, J. T. (2001). An Invariant Approach to Statistical Analysis of Shapes; Interdisciplinary Statistics. Florida, USA: CRC Press. Maximum Likelihood (2007). http://mathworld.wolfram.com/MaximumLikelihood.html Peter Tryfos (2001). York University, California (2007). http://www.yorku.ca/ptryfos/f1500.pdf Reflection. http://mathworld.wolfram.com/Reflection.html Schikuta, E. (1993). Grid Clustering: A Fast Hierarchical Clustering Method for Very Large Data Sets. Rice University, Houston. Shlens, J. (2003). A Tutorial on Principal Component Analysis. Smith, L. I. (2002). A Tutorial on Principal Component Analysis (2007). http://csnet.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf Taher Buyong (2006). Spatial Statistics for Geographic Information Science. Unit Penerbitan UTM.
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	RZ Other systems of medicine
spellingShingle	RZ Other systems of medicine Md. Hussin, Mohd. Bakery Analisis statistik bentuk untuk pengkelasan data kraniofasial
description	Statistical shape analysis can be applied in many disciplines including medical field. In this study, the statistical shape analysis is employed for classification of craniofacial data. The craniofacial data of students are obtained using the Konica Minolta Vivid 910 laser scanner to produce the three dimensional craniofacial image. The three dimensional coordinates of the surface for each landmark are obtained digitally using RapidForm 2004 software. The methods used are Principal Component Analysis (PCA) and Generalized Procrustes Analysis (GPA), to obtain average configuration of facial point transformation and facial point average, respectively. The craniofacial data are divided into sex and age categories by using the statistical classification method. The craniofacial data are classified by three methods: (i) Procrustes examination analysis of 35 students with 18 landmarks each; (ii) Procrustes examination analysis of 76 data sets with 27 landmarks each, and; (iii) Examination analysis of the 35 data sets with 20 distance measurements each. The results showed that the Procrustes values of male data are larger than the female data, and the Procrustes values of those born in 1989 were larger than those born in 1991. Centroid size differences between the sex and age are significant, hence the values of PCA and GPA can be used for classification analysis. The research results produced the average value for craniofacial data based on age and sex.
format	Thesis
qualification_level	Master's degree
author	Md. Hussin, Mohd. Bakery
author_facet	Md. Hussin, Mohd. Bakery
author_sort	Md. Hussin, Mohd. Bakery
title	Analisis statistik bentuk untuk pengkelasan data kraniofasial
title_short	Analisis statistik bentuk untuk pengkelasan data kraniofasial
title_full	Analisis statistik bentuk untuk pengkelasan data kraniofasial
title_fullStr	Analisis statistik bentuk untuk pengkelasan data kraniofasial
title_full_unstemmed	Analisis statistik bentuk untuk pengkelasan data kraniofasial
title_sort	analisis statistik bentuk untuk pengkelasan data kraniofasial
granting_institution	Universiti Teknologi Malaysia, Faculty of Geoinformation and Real Estate
granting_department	Faculty of Geoinformation and Real Estate
publishDate	2010
url	http://eprints.utm.my/id/eprint/16273/7/MohdBakeryHussainMFKSG2010.pdf
_version_	1747815003453390848

Analisis statistik bentuk untuk pengkelasan data kraniofasial

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