Preface Thesis RebeccaVTaylor

8/8/2019 Preface Thesis RebeccaVTaylor

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ANALYSIS OF THREE-DIMENSIONAL

CRANIOFACIAL IMAGES: APPLICATIONS IN

FORENSIC SCIENCE, ANTHROPOLOGY ANDCLINICAL MEDICINE.

ByREBECCA V TAYLOR

BSc (Hons) Grad Dip Ed

Submitted in total fulfilment

of the requirements of the degree of

Doctor of Philosophy

March 2008

School of Dental Science

The University of Melbourne

Australia


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Abstract


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The principal aims of this thesis were to use digital 3D craniofacial data to measure

the difference between individuals in different populations, to provide a detailed holistic

description of the population at large, as well as to investigate the feasibility of

classifying individuals in different populations based on the morphology of their

craniofacial features.

In this thesis, three empirical studies were undertaken using a variety of digital 3D

craniofacial images. Each study required digital 3D data of either the skull and/or the

face to be acquired, represented in different forms and analysed morphometrically.

The first study developed a method and recorded the results from soft tissue depth

data collected from clinical X-ray computer tomography images of the head. Digital 3D

images of the face and skull were acquired from 63 deceased adult males and the results

for differing age groups and ponderal states recorded. The perpendicular distance

between the skull and its overlying soft tissue surface (i.e. tissue depth) was measured

at 34 anatomical landmarks for every individual. Statistically significant differences

were only found between the weight subgroups, normal and overweight, overweight and

obese and normal and obese; no statistically significant differences were found between

persons of different age. The results of this study have shown that the acquisition of

head and neck data using the Aquilion 16 combined with amira and Geomagic

Qualify software was an extremely effective method for measuring the depth of the

soft tissue surface overlying the skull. Due to the absence of piercing or indentation of

the skin, as used in other methods, the current method described in this thesis provides a

suitable method for predicting the soft tissue surface of the face, required as the

foundation for forensic facial approximation.


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The second study described and classified individuals into one of two populations in

an anthropological collection. This study captured the 3D facial images of, and then

visualised, described, measured and classified two distinct ancestral groups, Asian and

Caucasian, which were further divided according to their gender. On each 3D facial

image a sparse set of landmarks (18) and a dense set of landmarks (9327) were defined

and identified. From the analyses applied to both the sparse and dense landmark sets,

principal component analysis of the densely corresponding sets of landmarks on the 3D

facial images of the anthropological collection was the more detailed and discriminatory

method for describing gender and ancestral differences. The average densely

corresponding 3D images defining each ancestral and gender group were clear tools for

the visualisation and modelling of differences between the groups. The most accurate

method for the classification of the anthropological collection was found after

undertaking a discriminant analysis with cross validation using the 25 principal

componenet scores from the dense landmark set that were found to have at least one

statistically significant difference between the four groups. This fully automated process

provided a total correct classification of 95% (range: 92 100%) of the anthropological

collection (195 of 206) and no incorrect classification of any individual to a group that

did not share at least one major characteristic, either gender or ancestry. Therefore,

ancestry and gender of individuals in the anthropological collection could be predicted

with a 95% accuracy using a digital 3D facial image. A major advantage of this

classification process was its complete independence from any form of human

judgement as the classification process was fully automated.


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The final experiment investigated the task of attempting to lateralise the source of the

seizure of patients with temporal lobe epilepsy (TLE), a condition with facial

manifestations. This study captured two digital 3D facial images, a neutral and

voluntary contraction pose, from four different subgroups. These subgroups consisted of

16 patients with idiopathic generalised epilepsy (IGE); 28 patients with partial epilepsy

on either the right (R-TLE) or left (L-TLE) side of the brain; as well as a control group

of participants with no epileptic condition (28). Classification of these individuals into

the four groups was done using the statistically significant principal component scores

calculated from a principal component analysis on the normalised deformation fields.

Correct classification occurred with 58% of all individuals in the epilepsy sample

placed into their correct group, a figure that increased to 64% (18 of 28 patients) when

only the TLE individuals were classified i.e. 60% of the L-TLE patients and 69% of the

R-TLE patients. Although both of these classification results were better than chance,

the classification results are still quite low and further work is required to improve them.

It is predicted that if the time taken for the capture of fleeting facial expressions in 3D

could be shortened then the power to lateralise the side of the epileptogenic lesion in the

TLE patients would strongly increase.

Finally, the current movement in clinical medicine and forensic science towards

implementing hardware that now routinely acquires the morphometric characteristics of

the craniofacial complex and represents them in digital 3D data presents more exciting

opportunities for the future. The results of this thesis have enabled a greater

understanding of the acquisition, representation and analysis of digital 3D craniofacial

data. As a result of the awareness provided by this major body of work, many diverse

fields may benefit. These include video surveillance, diagnosis of syndromes affecting

the craniofacial region, planning and assessment of orthodontic treatments andcraniofacial surgery, forensic science particularly the approximate reconstruction of the

facial features of deceased individuals from their remnant skull evidence, prediction of

facial features for archaeological remains displayed in museums, etc.


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Declaration

This is to certify that

i. The thesis comprises only my original work towards the PhD;

ii. Due acknowledgement has been made in the text to all other materials used; and

iii. The thesis is less than 100,000 words in length, exclusive of tables, figures,

references cited and appendices.

Rebecca V Taylor

Date: 26 th March, 2008


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Acknowledgements

I extend my thanks to Professor John Clement for supervising this thesis and

providing me with advice, knowledge and encouragement. I would also like to thank Mr

David Thomas, a co-supervisor.

My greatest debt of gratitude is to Dr. Peter Claes, for providing invaluable guidance

and instilling clarity and structure to this work. It was Peters programming skills that

enabled me to undertake the dense correspondence of the digital 3D facial images.

The staff and students of the School of Dental Science have been wonderful and have

made coming into study the most pleasant of experiences. I would also like to thank

Professor Eric Reynolds, head of the school for allowing me to undertake my research

at the school. Furthermore, Dr Sherie Blackwell and Ms Jacqueline Hislop-Jambrich

have been great friends and avid proof readers of my thesis.

I would like to thank Ms Grit Schlotthauer, an exchange student from The University

of Applied Sciences in Giessen-Friedberg in Germany for her enthusiasm and insight

towards data acquisition from the CT images at the VIFM.

The support of Dr Mineo Yoshino and Dr Sachio Miyasaka and the National Institute

of Police Science in Japan is greatly valued. Their donation of the Fiore surface scanner

to the School of Dental Science contributed greatly to this study. Thanks also to Tanijiri

Toyohisa, Medic Engineering, Japan, for his donation of the 3D-Rugle3 software.

Associate Professor Ian Gordon of the Statistical Consulting Centre, The University

of Melbourne provided his expertise and consultation in statistical analysis of my data.

The Victorian Institute of Forensic Medicine, in particular, Professor Stephen

Cordener, the Institutes Director allowed me to use data collected from their X-ray

computer tomography scanner. I would also like to thank Dr Chris ODonnell for his

assistance with the equipment.


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Professor Mark Cook and Dr Udaya Seneveratne of the Neurology department at St

Vincents Hospital, Melbourne assisted me with the collection of participants and

guidance within the epilepsy and facial asymmetry study in this thesis. Also to Dr Ross

Carne of the Neurology department at Geelong Hospital who allowed me to come to his

clinic to collect both patient and control data.

I gained impetus from discussions with Dr Robin Hennessey of the Royal College of

Surgeons in Ireland, Peter Hammond of University College London, Dr Caroline

Wilkinson of Dundee University in Scotland, and Professor Dirk Vandermeulen and Dr

Sven deGreef of Katholieke Universiteit in Leuven.

This thesis would not have been possible without the cooperation of all the

participants. I am extremely grateful for their assistance and enthusiasm.

Financial assistance for parts of this thesis was granted by the School of Dental

Science at The University of Melbourne, the Australian Dental Research Foundation

(ADRF) and the Australian Research Council (ARC). The generosity of these

institutions has made this thesis possible.

Without the love, support and patience of family and dear friends this work would not

have been completed. Special thanks must be given to my grandfathers, Pappy and

Poppy as well as my sister, Nicole for believing in me and supporting me during each

and every step. To my mum, Pam, thankyou for being there for me, feeding me and

cleaning up after me. Your support is infinite and unconditional and for that I am

extremely grateful. Finally, I must thank Andrew for his love, encouragement and the

many hours he spent proof reading each chapter.


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Table of Contents

ABSTRACT ....................................................................................................................iii

DECLARATION ............................................................................................................vi

ACKNOWLEDGEMENTS ..........................................................................................vii

TABLE OF CONTENTS ...............................................................................................ix

LIST OF FIGURES ......................................................................................................xiv

LIST OF TABLES ......................................................................................................xvii

LIST OF GRAPHS .......................................................................................................xix

LIST OF ABBREVIATIONS .......................................................................................xx

SCIENTIFIC PUBLICATION OF RESULTS ..........................................................xxi

CHAPTER ONE: INTRODUCTION............................................................................1

1.1 J USTIFICATION OF THESIS ...............................................................................................1

1.2 C ENTRAL AIM ..............................................................................................................2

1.3 R ESEARCH QUESTIONS ...................................................................................................2

1.4 D ESIGN OF THESIS ........................................................................................................2

CHAPTER TWO: LITERATURE REVIEW..............................................................7

PART A: FORENSIC FACIAL APPROXIMATION.................................................8

2.1 I DENTIFICATION OF HUMAN REMAINS ...............................................................................8

2.2 F ORENSIC FACIAL APPROXIMATION ..................................................................................9

2.3 H ISTORY OF FACIAL APPROXIMATION .............................................................................10

2.4 A HISTORY OF SOFT TISSUE DEPTH MEASUREMENTS ..........................................................20

2.5 T HE DEVIL LIES IN THE DETAILS ................................................................................25

2.6 T ESTING FACIAL APPROXIMATIONS ................................................................................26

2.7 M ETHODS FOR MAXIMISING IDENTIFICATION ....................................................................29

2.8 C ONCLUSION FOR FORENSIC FACIAL APPROXIMATION ........................................................31


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PART B: DEFINING ANCESTRY & GENDER.......................................................33

PREFACE TO PART B: D ISCUSSION ON TERMS DEFINING ANTHROPOLOGICAL COLLECTION ..............33

2.9 A NCESTRY AND GENDER : W HY MEASURE THE FACE ?......................................................35

2.10 B RIEF HISTORY OF ANTHROPOMETRIC MEASUREMENTS ....................................................37

2.11 3D SURFACE SCANNERS .............................................................................................44

2.12 V OLUMETRIC SCANNERS ............................................................................................45

2.13 R EPRESENTATION OF 3D FACIAL DATA ........................................................................47

2.14 C LASSIFICATION USING 3D FACIAL IMAGES ..................................................................48

2.15 C ONCLUSION FOR DEFINING ANCESTRY AND GENDER ......................................................49

PART C: EPILEPSY AND FACIAL ASYMMETRY...............................................50

2.16 E PILEPSY ................................................................................................................50

2.17 F ACIAL ASYMMETRY ................................................................................................55

2.18 C ONCLUSION FOR EPILEPSY AND FACIAL ASYMMETRY .....................................................60

CHAPTER THREE: 3D DATA ACQUISITION.......................................................61

3.1 I NTRODUCTION ...........................................................................................................61

3.2 F IORE , NEC J APAN ...................................................................................................62

3.2.1 Accuracy and reproducibility of Fiore...........................................................68

3.2.1.1 Summary of results for accuracy and reproducibility of Fiore................70

3.3 VIVID 910, K ONICA M INOLTA .................................................................................72

3.3.1 Accuracy and reproducibility of VIVID 910..................................................76

3.3.1.1 Summary of results and conclusions for accuracy and reproducibility of

VIVID 910...........................................................................................................79

3.4 A QUILION CT SCANNER , T OSHIBA ................................................................................813.4.1 Processing DICOM files................................................................................82

3.5 C OMPARATIVE SUMMARY OF DATA ACQUISITION TECHNIQUES USED IN THIS THESIS ................87

3.6 D ISCUSSION AND CONCLUSION ......................................................................................88


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CHAPTER FOUR: PROCESSING OF DATA IN 3D REPRESENTATION,

ANALYSIS AND DATA REDUCTION.....................................................................91

4.1 I NTRODUCTION ...........................................................................................................91

4.2 R AW 3D IMAGES .......................................................................................................93

4.3 O UTLINES (PROFILES ) THROUGH ANATOMICAL LANDMARKS AND SPARSE SETS OF ANATOMICAL

LANDMARKS ....................................................................................................................95

4.3.1 Collecting profile data...................................................................................96

4.3.2 Analysis of profile data..................................................................................98

4.3.3 Collecting a sparse set of anatomically corresponding landmarks.............100

4.3.4 Analysis of sparse landmarks.......................................................................102

4.3.5 Selection of the most relevant data from the sparse landmark set...............1054.4 D ENSE SET OF ANATOMICALLY CORRESPONDING LANDMARKS ...........................................106

4.4.1 Collecting a dense set of anatomically corresponding landmarks............. .110

4.4.2 Analysis of dense corresponding landmarks and selection of the most

relevant data.........................................................................................................111

4.5 C OMPARATIVE SUMMARY OF DATA PROCESSING USED THROUGHOUT THIS THESIS .................112

4.6 D ISCUSSION .............................................................................................................114

CHAPTER 5: APPLICATION FORENSIC FACIAL APPROXIMATION....... .117

5.1 I NTRODUCTION .........................................................................................................117

5.2 T HE FORENSIC FACIAL APPROXIMATION (FFA) SAMPLE ..................................................119

5.3 D ATA ACQUISITION AND ANALYSIS ..............................................................................121

5.4 R ESULTS .................................................................................................................125

5.5 D ISCUSSION .............................................................................................................131

5.6 C ONCLUSION ...........................................................................................................135

CHAPTER 6: APPLICATION ANCESTRAL AND GENDER

CLASSIFICATION.....................................................................................................137

6.1 I NTRODUCTION .........................................................................................................137

6.2 T HE ANTHROPOLOGICAL COLLECTION ...........................................................................140

6.3 S PARSE LANDMARKS .................................................................................................140

6.3.1 Euclidean distance measurements...............................................................142

6.3.1.1 Discriminant analysis using Euclidean distance matrix analysis..........146


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6.3.2 Conventional Euclidean distance measurements.........................................147

6.3.2.1 Discriminant analysis using conventional Euclidean distance

measurements....................................................................................................154

6.3.3 GPA and PCA of sparse landmark set.........................................................156

6.3.3.1 Discriminant analysis using PC scores from the sparse landmark set...161

6.4 D ENSE CORRESPONDENCE OF LANDMARKS ....................................................................164

6.4.1 Discriminant analysis using PC scores from the dense landmark set...... ...169

6.5 D ISCUSSION .............................................................................................................171

6.5.1 Summary of Results......................................................................................171

6.5.2 Discussion: Description techniques.............................................................172

6.5.3 Discussion: Classification............................................................................174

6.6 C ONCLUSION ...........................................................................................................175

CHAPTER SEVEN: APPLICATION EPILEPSY CLASSIFICATION...............177

7.1 I NTRODUCTION .........................................................................................................177

7.2 M ATERIALS : EXPLANATION OF SUBJECT PARTICIPANTS ...................................................179

7.2.1 Sample..........................................................................................................179

7.2.2 Photography and data storage of individuals in the epilepsy and facial asymmetry study....................................................................................................179

7.3 R AW 3D FACIAL IMAGES : I NTERSURFACE DISTANCE ......................................................180

7.3.1 Results: intersurface distance on raw 3D facial images..............................183

7.3.2 Summary of findings for intersurface distance............................................187

7.4 S PARSE LANDMARK DATA ..........................................................................................188

7.4.1 Results: sparse landmark data.....................................................................191

7.5 D ENSE CORRESPONDENCE .........................................................................................198

7.5.1 Results: dense landmark data......................................................................200

7.6 D ISCUSSION .............................................................................................................203

7.7 C ONCLUSION ...........................................................................................................209

CHAPTER EIGHT: CONCLUSIONS AND FUTURE DIRECTIONS................211

8.1 D ATA ACQUISITION DEVICES .......................................................................................211

8.2 3D IMAGING SOFTWARE ............................................................................................2148.3 F ORENSIC FACIAL APPROXIMATION ..............................................................................215


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8.4 A NCESTRY AND GENDER ............................................................................................217

8.5 E PILEPSY ................................................................................................................219

8.6 O VERALL FUTURE DIRECTIONS FOR 3D FACIAL ANALYSIS ...............................................222

8.6.1 Psychological testing...................................................................................222

8.6.2 DNA mapping...............................................................................................223

8.7 F INAL CONCLUSION ..................................................................................................224

REFERENCES............................................................................................................227

APPENDICES..............................................................................................................253

APPENDIX ONE ....................................................................................................................253APPENDIX TWO ...................................................................................................................261

APPENDIX THREE .................................................................................................................273

APPENDIX FOUR ..................................................................................................................289

APPENDIX FIVE ..................................................................................................................309

APPENDIX SIX .....................................................................................................................317


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List of Figures

Figure 2.1: The American 3D method by Betty Pat. Gatliff ...................................13

Figure 2.2: 2D facial approximation by Karen Taylor ..............................................14

Figure 2.3: Manual facial reconstruction using the Manchester method ................15

Figure 2.4: The reconstructed male face using Vanezis model for computerised 3D

facial modelling ......................................................................................................17

Figure 2.5: Three different approximations from the one skull using statistical

computerised modelling. .......................................................................................18

Figure 2.6: Digital sculpture system. ...........................................................................19

Figure 2.7: Two-tangent nasal projection method developed by Gerasimov ..........25

Figure 2.8: A chart from Bertillon's Identification anthropomtrique (1893 )..........39

Figure 2.9: Bertillonage measurements .......................................................................40

Figure 2.10: 3D facial archetypes. ................................................................................49

Figure 2.11: Epilepsy flowchart ...................................................................................53

Figure 2.12: Examples of the two types of bilateral symmetry .................................57

Figure 2.13: Analysis of shape variation in a 2D structure with object symmetry. 58

Figure 2.14: Example of the images used to determine the perceptual effects of

asymmetries in the expression of emotion. ..........................................................59

Figure 3.1: Example of 2D photographic distortion. ..................................................62

Figure 3.2: Fiore detector .............................................................................................63

Figure 3.3: Diagrammatic representation of how Fiore works. ................................64

Figure 3.4: Frankfort horizontal plane .......................................................................64

Figure 3.5: Defining the 3D face. ..................................................................................65

Figure 3.6: Fiore & Argus 3D data acquisition system ..............................................66

Figure 3.7: Image preview capture; ideal position for Fiore .....................................67

Figure 3.8: Image preview capture; bad position for Fiore .......................................67

Figure 3.9: The model head with 8 anatomical landmarks ............................68

Figure 3.10: The VIVID 910 .........................................................................................72

Figure 3.11: Measuring principle of the VIVID 910 ..................................................72

Figure 3.12: Scanning options selected in Geomagic Qualify to run VIVID 910 73

Figure 3.13: Manual Registration of 3 facial scans ....................................................74

Figure 3.14: Final registered and merged 3D facial image ........................................76Figure 3.15: Head cast model showing location of the 14 points ...............................77


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Figure 3.16: Registration of 3D facial scans ................................................................78

Figure 3.17: Toshiba Aquilion CT scanner .................................................................81

Figure 3.18: Projection view of CT data .....................................................................83

Figure 3.19: Image segmentation in amira. .................................................................84

Figure 3.20: 3D surface of the skull as viewed in amira ............................................85

Figure 3.21: 3D volume file of the soft tissue surface of the face ..............................86

Figure 4.1: Anatomical correspondence.........................................................................92

Figure 4.2: European male average from 35 faces.........................................................94

Figure 4.4: The profile extraction tool: vertical.............................................................97

Figure 4.5: The profile extraction tool: transverse.........................................................97

Figure 4.6: The function window of the Fourier Shape Descriptor software.................99

Figure 4.7: Identification of landmark gonion.............................................................101

Figure 4.8: Landmark detection...................................................................................102

Figure 4.9: EDMA demonstration................................................................................104

Figure 4.10: Registration of the 24 facial landmarks from 206 individuals using

Procrustes superimposition....................................................................................105

Figure 4.11: Representation of features with changing number of pseudo-landmarks108

Figure 5.1: Flowchart of the representation, analysis and data selection as applied to the

collection of soft tissue depth data for forensic facial approximation 118

Figure 5.2: Cases admitted to the VIFM during 2002 .............................................120

Figure 5.3: Flowchart of method for the collection and analysis of CT data .........122

Figure 5.4: The skull with all the chosen landmarks ...............................................124

Figure 5.5: Colour deviation map showing the intersurface distance ....................125

Figure 5.6: Results of the t-test between soft tissue depths of the BMI subgroups of

normal and overweight. ........................................................................................127

Figure 5.7: Results of the t-test between soft tissue depths of the BMI subgroups of normal and obese ...................................................................................................128

Figure 5.8: Results of the t-test between soft tissue depths of the BMI subgroups of

overweight and obese ............................................................................................129

Figure 6.1: Flowchart of the representation, analysis and data selection as applied to

individuals in the anthropological collection139

Figure 6.2: The 24 landmarks used in the ancestry and gender classification

application ............................................................................................................142Figure 6.3: Significant Euclidean distance pairs found through an EDMA. .........145


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Figure 6.4: Comparing Euclidean distances between Caucasian males and females

...............................................................................................................................149

Figure 6.5: Comparing Euclidean distances between Asian males and females ...150

Figure 6.6: Comparing Euclidean distances between FA and FC ..........................151

Figure 6.7: Comparing Euclidean distances between MA and MC .......................151

Figure 6.8: Registration of the 24 facial landmarks from 206 individuals using

Procrustes superimposition. ...............................................................................157

Figure 6.9: The affect of varying the first three PC scores on the mean sparse

landmark face of individuals in the anthropological collection. .....................160

Figure 6.10: Average of the anthropological collection at PC0 for the dense

landmark set .........................................................................................................165

Figure 6.11: Modelled PC data for the dense landmark set ....................................167

Figure 6.12: Average faces for the dense landmark set.. .........................................169

Figure 7.1: Flowchart of the representation, analysis and data selection as applied to

individuals in the epilepsy and facial asymmetry study.......................................178

Figure 7.2: Neutral and VE pose ................................................................................180

Figure 7.3: Measuring intersurface distance using 3D-Rugle3. ..............................182

Figure 7.4: 14 landmarks selected for epilepsy study ..............................................188

Figure 7.5: Average of the normalised deformation field in x, y and z-axis ...........200


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List of Tables

Table 2.1: The four categories present in forensic identification. ...............................9

Table 2.2: Previous research and methods for collection of soft tissue depths ........24

Table 3.1: Accuracy and reproducibility of Fiore. .....................................................71

Table 3.2: File extensions and their full file names ....................................................75

Table 3.3: Accuracy and reproducibility of VIVID 910. ...........................................80

Table 3.4: Reliability of measurements in Fiore and VIVID 910 ..............................80

Table 3.5: Summary of 3D data acquisition techniques used in this thesis .............87

Table 4.1: Summary of data processing ....................................................................112

Table 5.1: Landmark number, name and description .............................................123

Table 5.2: Comparison of the mean and standard deviation of the soft tissue depths

for the total males and the 3 different BMIs studied ......................................126

Table 5.3: Comparing the mean and standard deviation of the soft tissue depths

for the total males and the 3 different age groups studied.. ............................130

Table 5.4: Historical and comparative perspective soft tissue depth studies of adult

males. ....................................................................................................................134

Table 6.1: Experimental sample the anthropological collection ..........................140

Table 6.2: Landmark numbers, definition and location in the face for the sparse

landmark set .........................................................................................................141

Table 6.3: Layout required by PAST ........................................................................143

Table 6.4: Discriminant analysis using 44 Euclidean distance pairs. .....................146

Table 6.5: Conventional Euclidean measurements between the 24 landmarks .....148

Table 6.6: Number, mean and standard deviation of measurement in each group.

...............................................................................................................................152

Table 6.7: Discriminant analysis of FA, FC, MA & MC using 35 Euclidean

distances ................................................................................................................155

Table 6.8: PCA of Procrustes residuals describing craniofacial shape variability

within the sample of 206 individuals ..................................................................158

Table 6.9: Discriminant analysis using first 13 PCs of shape variation .................162

Table 6.10: Discriminant analysis using first 13 PCs of shape variation and

centroid size ..........................................................................................................163

Table 6.11: Discriminant analysis using all 65 PCs of shape variation and centroidsize .........................................................................................................................163


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Table 6.12: Discriminant analysis using Centroid Size and 24 significant PCs ....164

Table 6.13: Discriminant analysis using PC 1 -13 above average eigenvalue .......170

Table 6.14: Discriminant analysis using 25 significant PCs ....................................170

Table 6.15: Summary of the description and classification results for this

anthropological collection ...................................................................................171

Table 7. 1: Epilepsy study sample ..............................................................................179

Table 7.2: Descriptive statistics of asymmetry score for the neutral and VE pose

...............................................................................................................................192

Table 7.3: T-tests between movement scores of corresponding landmarks ...........197

Table 7.4: Classification table of normalised deformation fields using PC 1 11 202

Table 7.5: Classification table of normalised deformation field using 15 significant

PCs ........................................................................................................................202

Table 7.6: Classification table using PC 22 on the normalised deformation field of

the TLE patients ..................................................................................................203

Table 7.7: Summary of methods and results from previous research into symmetry

of the face as a localisation and lateralisation tool for partial epilepsy. .........208


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List of Graphs

Graph 6.1: PC1 vs Centroid Size ...............................................................................159

Graph 7.1: Boxplot showing difference between the left and right sides of the face

when comparing the intersurface distance when the VE face is superimposed

on the neutral face. ..............................................................................................183

Graph 7.2: Scatterplot showing the relationship between the left and right

intersurface distance when the VE face is superimposed on the neutral face.

...............................................................................................................................184

Graph 7.3: Probability plot showing the distribution of values for the difference

between the intersurface distance on the left (L) and right (R) sides of the face.

...............................................................................................................................185

Graph 7.4: Bartletts test for equal variation ...........................................................186

Graph 7.5: Bartletts test for equal variance- combined TLE ................................187

Graph 7.6: Boxplot of asymmetry scores for the neutral and VE 3D facial images

using group as a variable. ...................................................................................191

Graph 7.7: Boxplot showing the difference in the asymmetry score when the

neutral asymmetry score is subtracted from the VE asymmetry score. ........193

Graph 7.8: Boxplot of overall movement scores. ......................................................194

Graph 7.9: Boxplot showing comparison of the average movement score between

the left and right sides of the face .......................................................................195

Graph 7.10: Boxplot comparing the difference of the average movement score

from the right side and left sides of the face. ....................................................196

Graph 7.11: Boxplot showing the difference in the movement score of the left and

right cheilion for each of the groups in the Epilepsy study. ............................197


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List of Abbreviations

2D Two-dimensional

3D Three-dimensional

ANOVA Analysis of Variance

BMI Body Mass Index

CBCT Cone Beam Computer Tomography

CT Computer Tomography

DF Deformation Field

DICOM Digital Imaging and Communications in Medicine

DNA Deoxyribonucleic acid

ED Euclidean Distance

EDMA Euclidean Distance Matrix Analysis

EEG Electroencephalogram

EFSA Elliptic Fourier Shape Analysis

F Female

FA Female Asian

FC Female Caucasian

FFA Forensic Facial Approximation

FSA Fourier Shape Analysis

GPA Generalised Procrustes AnalysisHU Hounsfield Units

ID Intersurface Distance

IGE Idiopathic Generalised Epilepsy

LM Landmark

M Male

MA Male Asian

MC Male Caucasian

MRI Magnetic Resonance ImagingOA Other Ancestral

PC Principal Component

PCA Principal Component Analysis

RGB Red-Green-Blue

SA Same Ancestral

TLE Temporal Lobe Epilepsy

VE Voluntary Expression

VIFM Victorian Institute Of Forensic Medicine


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Scientific Publication of Results

Cook MJ, DSouza W, Carne R, Clement JG, Thomas DL, Morris K, Taylor RV ,

Seneviratne U. Facial asymmetry: a useful lateralizing sign in partial epilepsy as

measured by computer-assisted 3D facial morphometry. Poster presented at the 20 th

Annual Scientific Meeting of the Epilepsy Society of Australia, Cairns, Australia,

13 15 April, 2005.

Taylor RV , Thomas CDL, Clement JGC. The distribution of information content

across the human face a study using Fourier shape analysis. Poster presented at

the 17 th Meeting of the International Association for Forensic Science, Hong Kong

21 26 August, 2005


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Preface Thesis RebeccaVTaylor

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