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Exploring Variation in Three-Dimensional Shape Data
A variety of very useful methods of statistical shape analysis are available for landmark data. In particular, standard methods of multivariate analysis can often be applied after suitable alignment and transformation of the data. An important example is the use of principal components analysis to p...
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Published in: | Journal of computational and graphical statistics 2006-09, Vol.15 (3), p.524-541 |
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container_title | Journal of computational and graphical statistics |
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creator | Bowman, Adrian W Bock, Mitchum T |
description | A variety of very useful methods of statistical shape analysis are available for landmark data. In particular, standard methods of multivariate analysis can often be applied after suitable alignment and transformation of the data. An important example is the use of principal components analysis to provide a convenient route to graphical exploration of the main modes of variation in a sample. Where there are many landmarks or shape information is extracted in the form of curves or surfaces, the dimensionality of the resulting data can be very high and it is unlikely that substantial proportions of variability will be captured in one or two principal components. Issues of graphical exploration are explored in this setting, including random tours of a suitable low-dimensional subspace, the comparison of different groups of data, longitudinal changes and the identification of the features which distinguish individual cases from a group of controls. A suitable software environment for handling these methods with three-dimensional data is outlined. Issues of comparing principal components across time are also tackled through appropriately constructed permutation tests. All of these techniques are illustrated on a longitudinal study of facial development in young children, with particular interest in the identification of differences in facial shape between control children and those who have undergone surgical repair of a cleft lip and/or palate. |
doi_str_mv | 10.1198/106186006X136679 |
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Issues of comparing principal components across time are also tackled through appropriately constructed permutation tests. 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In particular, standard methods of multivariate analysis can often be applied after suitable alignment and transformation of the data. An important example is the use of principal components analysis to provide a convenient route to graphical exploration of the main modes of variation in a sample. Where there are many landmarks or shape information is extracted in the form of curves or surfaces, the dimensionality of the resulting data can be very high and it is unlikely that substantial proportions of variability will be captured in one or two principal components. Issues of graphical exploration are explored in this setting, including random tours of a suitable low-dimensional subspace, the comparison of different groups of data, longitudinal changes and the identification of the features which distinguish individual cases from a group of controls. A suitable software environment for handling these methods with three-dimensional data is outlined. Issues of comparing principal components across time are also tackled through appropriately constructed permutation tests. All of these techniques are illustrated on a longitudinal study of facial development in young children, with particular interest in the identification of differences in facial shape between control children and those who have undergone surgical repair of a cleft lip and/or palate.</description><subject>Children</subject><subject>Computer software</subject><subject>Coordinate systems</subject><subject>Covariance matrices</subject><subject>Curve data</subject><subject>Dimensionality</subject><subject>Facial modeling</subject><subject>Geometric shapes</subject><subject>Landmark data</subject><subject>Landmarks</subject><subject>Longitudinal studies</subject><subject>Mathematical vectors</subject><subject>Permutation tests</subject><subject>Principal components</subject><subject>Principal components analysis</subject><subject>Procrustes analysis</subject><subject>Shape analysis</subject><subject>Software</subject><subject>Statistical analysis</subject><subject>Studies</subject><subject>Tangents</subject><subject>Three dimensional imaging</subject><issn>1061-8600</issn><issn>1537-2715</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNp1kM1Lw0AQxYMoWKt3L0LwHp3Z78WTtPUDCh6s4m3ZNhu7JU3ibor2v3dLxYPgaYb5vfcYXpadI1whanWNIFAJAPGGVAipD7IBcioLIpEfpj3hYsePs5MYVwCAQstBRidfXd0G37znrzZ42_u2yX2Tz5bBuWLs166J6WTr_HlpO5ePbW9Ps6PK1tGd_cxh9nI3mY0eiunT_ePodlosKGN9IUEpIqhVlGtFmBV0jgAMGQOQmlVY8VISoFRBSQWUHBl3eo7MagraETrMLve5XWg_Ni72ZtVuQvolGkK5FJooTCLYixahjTG4ynTBr23YGgSza8b8bSZZLvaWVezb8KsnkmuGWiR-s-e-qdqwtp9tqEvT220qqgq2Wfho6L_p3_Xmbnw</recordid><startdate>20060901</startdate><enddate>20060901</enddate><creator>Bowman, Adrian W</creator><creator>Bock, Mitchum T</creator><general>Taylor & Francis</general><general>American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope></search><sort><creationdate>20060901</creationdate><title>Exploring Variation in Three-Dimensional Shape Data</title><author>Bowman, Adrian W ; Bock, Mitchum T</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c344t-7088263a8359824a63b100414400794f1f5d7203380d360d5145e9b14a9309e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Children</topic><topic>Computer software</topic><topic>Coordinate systems</topic><topic>Covariance matrices</topic><topic>Curve data</topic><topic>Dimensionality</topic><topic>Facial modeling</topic><topic>Geometric shapes</topic><topic>Landmark data</topic><topic>Landmarks</topic><topic>Longitudinal studies</topic><topic>Mathematical vectors</topic><topic>Permutation tests</topic><topic>Principal components</topic><topic>Principal components analysis</topic><topic>Procrustes analysis</topic><topic>Shape analysis</topic><topic>Software</topic><topic>Statistical analysis</topic><topic>Studies</topic><topic>Tangents</topic><topic>Three dimensional imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bowman, Adrian W</creatorcontrib><creatorcontrib>Bock, Mitchum T</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Journal of computational and graphical statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bowman, Adrian W</au><au>Bock, Mitchum T</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Exploring Variation in Three-Dimensional Shape Data</atitle><jtitle>Journal of computational and graphical statistics</jtitle><date>2006-09-01</date><risdate>2006</risdate><volume>15</volume><issue>3</issue><spage>524</spage><epage>541</epage><pages>524-541</pages><issn>1061-8600</issn><eissn>1537-2715</eissn><abstract>A variety of very useful methods of statistical shape analysis are available for landmark data. 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Issues of comparing principal components across time are also tackled through appropriately constructed permutation tests. All of these techniques are illustrated on a longitudinal study of facial development in young children, with particular interest in the identification of differences in facial shape between control children and those who have undergone surgical repair of a cleft lip and/or palate.</abstract><cop>Alexandria</cop><pub>Taylor & Francis</pub><doi>10.1198/106186006X136679</doi><tpages>18</tpages></addata></record> |
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subjects | Children Computer software Coordinate systems Covariance matrices Curve data Dimensionality Facial modeling Geometric shapes Landmark data Landmarks Longitudinal studies Mathematical vectors Permutation tests Principal components Principal components analysis Procrustes analysis Shape analysis Software Statistical analysis Studies Tangents Three dimensional imaging |
title | Exploring Variation in Three-Dimensional Shape Data |
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