A Geometric Morphometric Validation Study of Computed Tomography Extracted Craniofacial Landmarks

A Geometric Morphometric ValidationStudy of Computed TomographyYExtracted

Craniofacial Landmarks

Amanda R. Hale, MA, Kenda K. Honeycutt, MA, and Ann H. Ross, PhD

Abstract: This study investigates the variation between craniofaciallandmarks extracted from computed tomographic (CT) scans andthose collected from direct digitization of dry skulls. Thirteen tra-ditional craniofacial landmarks were obtained from each CT scanusing the coordinate option in the software Aviso. These coordinateswere then compared with the coordinates digitized directly from thedry skulls as 2 separate samples and individually. Similarities werefound between the 2 coordinate samples, with the first principalcomponent representing only 23.97% of the total variation associ-ated with the data acquisition methods, and were found to be sta-tistically significant (P = 0.0223). Differences were more prevalentalong the midline landmarks. In contrast, the individual specimencomparisons exhibited the largest amount of variation within thesymmetric landmarks with the bilateral landmarks that were moremedially located in the CT sample, but no individual specimens weresignificantly different (eg, P = 0.9883) when comparing both dataacquisition modalities. The bilateral coordinates were not found to besignificantly different for either analysis (P = 0.4165). The signifi-cant differences found for the entire data set suggest that the com-bination of CT-extracted and digitized individuals needs to be furtherexplored with respect to the reference frames and sample compo-sition. However, the individual specimen comparison results of thisstudy validate the utility of CT-extracted landmarks when used forputative identifications in a forensic setting and when clinicallyapplied.

Key Words: Computed tomography, craniofacial landmarks,geometric morphometrics

(J Craniofac Surg 2014;25: 231Y237)

Skeletal biologists have been examining skeletal landmarks as asource of quantifiable variation for well over a century.1Y5

Morphometrics has been the most common approach used toquantify evolutionary, genetic, and other forms of phenotypic vari-ation within and between populations.2,6Y9 Primarily, linear measuresof these human anatomic structures were used to investigate themorphologic variation among populations.10 In addition, morpho-metric studies are advantageous because of their inherently nonde-structive and noninvasive characteristics relative to other methods,such as molecular studies for determining genetic affiliation. Typi-cally, these studies have focused on the investigation of morphologicchanges caused by evolutionary forces or migration,11 but anothermajor aim of these studies was the development of standards forancestry estimation for forensic casework.12 More recently, thesebiologic landmarks have been collected in two-dimensional or three-dimensional space to quantify variation, applying more robust sta-tistical methodology.9,10,13Y15 This approach is supported by the ideathat each anatomic landmark holds significant information about itsprecise location on an anatomic structure and, together, can provide aspatial map of the structure.9,16,17

Geometric MorphometricsGeometric morphometrics (GM) refers to the methods that

capture the geometry of a form, analyze the landmark data config-urations, and provide graphical depictions of the statistical differ-ences present in the shape of a structure.6,18Y20 This approachquantifies and explains the morphologic variability in terms of astructures covariation.9 Location data collected as three-dimensionallandmark coordinates maintain a structure’s geometric integrity. Thisgeometry is the language used to describe and visualize form (eg,individual forms, pairs of forms, and the effects on the entire samplesof forms) and is combined to represent the landmark configuration ofa structure or form.10,16,21,22

The introduction of GM methods provided a novel way tovisually represent patterns of variation in a structure.23 Forensicapplications have included the development of population-specificstandards and the method of landmark data extraction to generatequantitative putative identifications from radiographs and otherforms of digital imaging.13,24Y27 The 2 most common landmarkacquisition methods are either computed directly from the specimenusing a three-dimensional digitizing instrument to provide the co-ordinates28 or uses the digital reproduction of an object and softwareto record the landmarks.9,29

Most studies using GM methods for the development ofpopulation-specific standards have directly digitized dry remainssuch as skulls.14,30,31 Although direct digitization is known for a highdegree of accuracy and precision, bias within many of these sampleschallenges their utility as references for the development of con-temporary standards.31Y34 Currently, only 3 reference collections inthe world are considered forensic or identified35 (Table 1). Identifiedcollections contain known demographic information on decedents

ORIGINAL ARTICLE

The Journal of Craniofacial Surgery & Volume 25, Number 1, January 2014 231

What Is This Box?A QR Code is a matrix barcode readable byQR scanners, mobile phones with cameras,and smartphones. The QR Code links tothe online version of the article.

From the Department of Sociology and Anthropology, North Carolina StateUniversity, Raleigh, North Carolina.

Received August 6, 2013.Accepted for publication August 27, 2013.Address correspondence and reprint requests to Amanda R. Hale, MA,

NCSU, 334 1911 Building, Campus Box 8107, Raleigh, NC 27695-8107E-mail: [email protected]

Supported by the National Institute of Justice grant no. 2005-MU-BX-K078.The authors report no conflicts of interest.Copyright * 2014 by Mutaz B. Habal, MDISSN: 1049-2275DOI: 10.1097/SCS.0000000000000460

Copyright © 2014 Mutaz B. Habal, MD. Unauthorized reproduction of this article is prohibited.

[email protected]

allowing their application to living populations. Most were amassedduring the second half of the 20th century, and all other referencesamples date from the late 18th century to the early 20th century. Thetemporal distance of these samples suggests that some of thesecollections may not accurately reflect the present-day variation ofcontemporary populations.12,36,37,50 This lack of ‘‘modern’’ referencecollections illustrates the need for additional resources that supple-ment the lack of contemporary skeletal material,32,38 which wouldalso have clinical implications for surgical reconstructions. Laurentet al29 found that each digital reconstruction and visualization ofcrania from computed tomographic (CT) slices takes approximately5 minutes per specimen. Moreover, the resolution of the digital re-constructions was comparable with CT resolution. Importantly, theexamination of CTs is routine for most cranial or cerebral problemsand digital reconstructions can assist in the development of surgicaltreatments.

Geometric morphometric methods do not necessitate data to beretrieved fromdry remains,which allow for digital images as a potentialdata source. Computed tomographic scans are a type of digital imagingthat contains detailed structural information, which can be readilyextracted. Integration between digital scans and visualization methodscan produce a structural model representing a structure’s reality withinthe original projected context.9 In addition, digital images are notaffected by a changing frame of reference, thus avoiding the error in-troducedwhencoordinatesmust undergooptimizationbefore analyses.32

However, the current lack of systematic studies validating CT dataacquisition currently prevents its wide-scale use.28

Computed TomographyComputed tomography has been praised for its advanced

image capture ability and its lack of geometric magnification error.39

Computed tomography captures information in slices, and theseslices can be combined to create a three-dimensional rendering.Coordinates derived from CT images are also free of any particularcoordinate system with a fixed frame of reference, thus increasingtheir efficiency in geometric morphometric studies by negating theneed for data transformations.40Y42 This property could potentiallyreduce the error introduced during the transformation process.

Studies have foundCT landmark identification and extraction tobe repeatable with a high degree of precision.32,39 Regardless of theirreported accuracy, expansion has been largely restricted to clinicalsettings and medical research until recently.27,29,40 Considering thatfew studies have examined the validity of CT-extracted landmarks,despite findings on their repeatability, caution is still recommendedwhen used in conjunction with data collected from direct digitization.Validation models permit the researcher to determine error introducedfrom multiple sources including a population’s morphologic variabil-ity.41 In an exploratory study, Ross et al42 found 21% of interlandmarkdistances produced fromCT images, exhibiting statistically significantshape differences relative to their digitized counterparts. In addition,these significant interlandmark distances were derived from type IIlandmarks (eg, zygomaxillare), supporting other studies that demon-strate the decrease in accuracywhen locating particular landmark typeson digital reconstructions.16 However, there was no systematic biasfound between CT- and digitized acquired coordinates.

The precision, accuracy, and repeatability of craniofacial land-marks extracted from CT have been explored elsewhere.28,31,32,39,43

However, their validation within a statistical framework has been lessstudied.With the increase of studies usingGMand the rise of advancedmedical imaging, more rigorous validation methods need to beimplemented to assess artifactual differences between ‘‘virtual’’ and‘‘real’’ specimens. If suitably validated, these data could potentiallyprovide a large source of reference information about contemporarypopulations.28,44 Thus, the main research objective of this study was toinvestigate the utility of craniofacial shape coordinates extracted fromCT scans in population variation analyses as compared with directlydigitized coordinates. Significant shape differences between coordi-nate data types will also be examined.

MATERIALS AND METHODS

Research DesignThe sample consists of 48 individuals of African ancestry

from the Samuel Morton collection, housed at the University ofPennsylvania’s Museum of Archaeology and Anthropology. Thirteen

TABLE 1. Number of various reference sample types available for study

Sample Type Number

Historic 3

Mixed 3

Cemetery 11

Forensic V W.Bass Forensic Collection, Medicina Legal Madrid,and Los Angeles Office of Chief Medical Examiner

3

TABLE 2. List of the Landmarks Used With Abbreviations

Cranial Landmark Abbreviation

1. Nasion nas

2. Left frontomalare fmal

3. Right frontomalare ftml

4. Subspinale ssp

5. Prosthion pr

6. Left dacryon dacl

7. Right dacryon dacr

8. Left zygomaxillare zygoml

9. Right zygomaxillare zygomr

10. Left asterion astl

11. Right asterion astr

12. Opisthion op

13. Basion bas

FIGURE 1. Craniofacial landmarks used in this study (anterior view).

Hale et al The Journal of Craniofacial Surgery & Volume 25, Number 1, January 2014

232 * 2014 Mutaz B. Habal, MD


traditional craniofacial landmarks were collected for this studyfor a total of 39 variables (13 � 3) for 3 dimensions (Table 2and Figs. 1Y3). Each cranium was digitized with a Microscribe G2Xdigitizer (Immersion Corporation, San Jose, CA) and scanned usingCT. The digitizer has been validated elsewhere.31 The CT scans ofthese digitized individuals are part of the Open Research Scan Ar-chive at the University of Pennsylvania. These were digitallyreconstructed as shown in Figure 4 using the software Aviso Stan-dard. Landmark coordinates were obtained from each reconstructeddigital image using the coordinate option (Fig. 4). Only standardtypes I and II landmarks were included for the study. Detailedlandmark descriptions are provided in the study of Howells.2 Theanalyses consisted of the following: (1) the digitized landmarks andCT-extracted coordinates were first treated as 2 separate groups toexamine for overall shape differences, and (2) differences were alsoassessed between digitized and CT-extracted coordinates for eachindividual cranium.

Statistical Analysis: Sample ComparisonTheMorphoJ shareware programwas used to run all statistical

analyses,45 which can be downloaded from http://www.flywings.org.uk/MorphoJ_page.htm. Procrustes superimposition was used totranslate, scale, and rotate the landmark data to bring all specimensinto a common coordinate system, which derives a consensus con-figuration using a least-square fit, allowing small shape changes to

FIGURE 2. Craniofacial landmarks used in this study (inferior view).

FIGURE 3. Craniofacial landmarks used in this study (posterior view).

FIGURE 4. Example of digitally reconstructed cranium using the Aviso Standard.

TABLE 3. First 9 PC Representing 91% of the Variation

PC Eigenvalues % Variance Cumulative %

1 0.00059071 23.791 23.791

2 0.00043177 17.390 41.181

3 0.00035413 14.262 55.443

4 0.00023950 9.646 65.089

5 0.00019355 7.795 72.884

6 0.00014735 5.935 78.819

7 0.00012681 5.107 83.926

8 0.00009688 3.902 87.828

9 0.00007993 3.219 91.047

The Journal of Craniofacial Surgery & Volume 25, Number 1, January 2014 CT-Extracted Craniofacial Landmarks

* 2014 Mutaz B. Habal, MD 233


http://www.flywings.org.uk/MorphoJ_page.htm

http://www.flywings.org.uk/MorphoJ_page.htm

be detected.22,46,47 New shape coordinates were derived for the entiredata set. The Procrustes plot was used to identify any outliers, andthese were subsequently removed. After the outliers were removed,the sample was reduced to 42 individuals. To assume homogeneitywithin each sample, covariance matrices were produced from theoriginal data set and all variances were pooled.45

Principal components analysis was performed to reduce thenumber of variables to a few dimensions representing most varia-tion.21,48 Principal components analysis does not account for groupstructure; thus, it explores all relations among groups of specimens toidentify obvious subdivisions, making it ideal to identify differencesin specimens on the basis of data acquisition type. Moreover, it issuitable because the principal component (PC) variance is equal tothe variance of the original data set.22,45

A discriminant function analysis (DFA) was performed todiscern the approximate level of separation between the 2 groups.Discriminant function analysis finds the shape features that have amaximum difference between groups relative to within-group vari-ation. Permutation tests (1000 permutation runs) were used for

resampling to account for differences between the 2 groups’ co-variance matrices.45 Cross-validation was performed to address theoverestimation of the separation in the DFA.

Procrustes analysis of variance (ANOVA) was performed toinvestigate the possible effects of symmetric landmarks.49 In addi-tion, a multivariate ANOVA (MANOVA) was performed to measurethe shape covariance between the 2 groups. Matrix correlation was per-formed to determine the degree of correlation between the digitizedand CT covariance matrices. All analyses account for object symmetry.

Statistical Analysis: Individual ComparisonThe individual comparison was performed tovalidate CTas an

option instead of direct digitization of a dry skull for use in theputative identification of an unknown individual. Only individualswith nomissing landmarks were used in this part of the study becausecomparisons were being made between individual skulls. The samplesize for this portion of the analysis totaled 22 individuals, or 46% ofthe original sample. The Procrustes superimposition was again usedto extract the shape coordinates. Principal components analysisexamined regions of variation between the 2 methods, and theProcrustes ANOVA was used to examine the variation betweenmethods for individual crania to identify significant deviations due tospecimen asymmetry. All analyses account for specimen symmetrywith analyses for midline and paired landmarks.

RESULTS

Sample ComparisonMost of the variation is represented by the first 9 PCs, which

represents 91% of the total variation with 23.79% of the variationfound along the midline (Table 3; Fig. 5). The midline landmarksnasion and subspinale are responsible for the variation seen betweenCTand dry skull samples (Fig. 6). This spread of variation across the9 PCs was expected because both groups were derived from the sameindividuals and they were of homogenous ancestry. The first PCseems to separate the groups by data acquisition type rather thanany biologic variation present in the sample. However, as seen inFigure 5, the separation due to data acquisition type is still quiteminimal. The second PC depicted in Figure 7 shows that most of thevariation is located laterally on the cranial vault at the landmarkasterion. Interestingly, all digitized individuals (Fig. 5) are tightlyclustered, suggesting that the effects of a changing frame of referencemay have a negligible effect on the data. In addition, the 95%

FIGURE 5. Plot of the first PC versus the second PC. Ellipses represent 95%confidence intervals.

FIGURE 6. Wireframe graph depicting the variation along the first PC (0.5�).




confidence interval for the CT-extracted landmarks is larger,suggesting possible error from the extraction process.

Discriminant function analysis had a misclassification rate of8% (Table 4), whereas the cross-validation provided a more expectedmisclassification rate of 48% (Table 5). The cross-validation illus-trates the similarities between the 2 groups of coordinates with aslightly better chance of separation. However, the DFA results couldbe confounded because of a probable increase in within-groupvariation rather than between-group variation, which would ac-count for the difference in the classification rates.43

Procrustes’ ANOVA found a significant difference (P =0.0002) between data acquisition methods related to individual shapeeffects when effects due to symmetry are controlled (Table 6). Inaddition, this difference seems more related to the within-groupvariation in the samples because symmetric effects were not foundto be significant (P = 0.4165). This supports the PCs analysis resultsbecause most variation was found along the midline and may suggesta higher degree of accuracy when extracting bilateral landmarks. TheMANOVA results (Table 7) corroborate this shape difference as well(P = 0.0223). Furthermore, the matrix correlation found 77.3% of thegroups covariance matrices to be significantly correlated (P =

0.0001), further illustrating the similarity of the 2 samples analyzed.However, pooling the individuals into 2 samples could confound the

FIGURE 7. Wireframe graph depicting the variation along the second PC (0.5�). Landmarks showing the greatest amount of variation are circled.

TABLE 6. Procrutes ANOVA Results for Sample Comparison (> = 0.05).

Effect Sum of squares df F P

Individual 0.003448 18 7.86 0.0002*

Side 0.000383 14 1.12 0.4165

Individual* side 0.000341 14 0.27 0.9989

Residual 0.130559 1472

*Represents significant values.

TABLE 7. Multivariate ANOVA Shape Results (> = 0.05)

Effect Pillai trace P

Individual 0.59 0.0223*

*Represents significant values.

TABLE 8. Analysis of Variance Results for 10 Average IndividualComparisons (> = 0.05)

ID No. Sum of squares df F P

M34 0.00039857 18 0.29 0.9924

M426 0.07301814 18 0.91 0.5776

M1336 0.00050073 18 0.36 0.977

M1859 0.00012623 18 0.32 0.9883

M901 0.0005342 18 0.63 0.8243

M908 0.00018769 18 0.33 0.9853

M913 0.00015398 18 0.47 0.933

M915 0.00030769 18 0.75 0.7208

M919 0.00092309 18 0.52 0.9025

M921 0.00031073 18 1.16 0.3965

TABLE 4. Discriminant Function Analysis Results (8%Misclassification Rate)

Group Three-dimensional CT Total

Three-dimensional 23 2 25

CT 2 21 23

TABLE 5. Discriminant Function Analysis Cross-Validation Results (48%Misclassification Rate)

Group Three-dimensional CT Total

Three-dimensional 13 12 25

CT 11 12 23




analysis because the individuals within each sample are differentfrom one another.

Individual ComparisonThe Procrustes ANOVA results for individual and symmetry

effect are presented for 10 individuals that are representative of all22 comparisons. No significant individual differences were foundbetween the CT-extracted and the directly digitized coordinates(Table 8). In addition, no individual was statistically significant forsymmetric effects (Table 9). The first PC wireframe graph for 1individual is shown in Figure 8. This wireframe is representative ofall other individual analyses. The first PC illustrates that the land-marks frontomalare and zygomaxillare are more variable from theCT-extracted coordinates, which are positioned more medially.However, the ANOVA results demonstrate that this variation is notsignificant. These results indicate the validity of using CT-acquired

coordinates used for forensic and clinical settings. Further, with nosignificant differences due to bilateral asymmetry, all 13 landmarksused in this study show a high degree of accuracy when used at theindividual level.

DISCUSSIONThe results of this study validate the utility of CT cranial

scans as a potential data source when implemented into theidentification process and clinical settings. However, at the pop-ulation level, additional research is necessary to determine thevalidity of combining data from these 2 different data acquisitionmethods, particularly regarding the error introduced from thechanging frame of reference in digitized samples. Although theycan be transformed, this process is not without its own biases.21

Moreover, a population’s inherent morphologic variation may notexceed the variation introduced by selection of data acquisitionmethod. This could potentially confound the actual sources ofvariation present. Further research to investigate the precision ofextracting landmarks located along the midline is warranted be-cause this study found the greatest variation along the midlinelandmarks (eg, nasion). However, this may be a by-product ofoptimization when object symmetry is present.49

In addition, because most of the differences were noted in thecraniofacial region, an assessment of how structure complexity af-fects the identification of anatomic landmarks is needed, althoughthis may be a result of a larger concentration of landmarks in thisregion. With increasing structure complexity, minimal error mayhave a more substantial effect on surgical reconstructions.50

ACKNOWLEDGMENTSThe authors thank Janet Monge from the University of

Pennsylvania for providing the CT files from the Morton Collection.The authors also thank Rebecca Sutphin for reconstructing the three-

TABLE 9. Analysis of Variance Results for 10 Average Individuals in the SideComparison (> = 0.05)

ID No. Sum of squares df F P

M34 0.000441561 14 0.41 0.9453

M426 0.077783 14 1.25 0.3396

M1336 0.00012174 14 0.36 0.977

M1859 0.00007636 14 0.25 0.9935

M901 0.00009503 14 0.14 0.9996

M908 0.00019305 14 0.44 0.9329

M913 0.00004023 14 0.16 0.9993

M915 0.00017903 14 0.56 0.8542

M919 0.00098099 14 0.71 0.7309

M921 0.00009827 14 0.47 0.9147

FIGURE 8. Wireframe graph depicting the variation along the first PC for individual M1859 (0.5�). The greatest area of variation is circled.




dimensional cranial images from the CT slices and extracting thecoordinates for all the individuals.

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A Geometric Morphometric Validation Study of Computed Tomography Extracted Craniofacial Landmarks

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