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International Journal of LegalMedicine ISSN 0937-9827Volume
129Number 3 Int J Legal Med (2015) 129:569-581DOI
10.1007/s00414-014-1074-1
Ground truth data generation for skullface overlay
O.Ibez, F.Cavalli,B.R.Campomanes-lvarez,C.Campomanes-lvarez,
A.Valsecchi &M.I.Huete
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TECHNICAL NOTE
Ground truth data generation for skullface overlay
O. Ibez & F. Cavalli & B. R. Campomanes-lvarez &C.
Campomanes-lvarez & A. Valsecchi & M. I. Huete
Received: 29 April 2014 /Accepted: 28 August 2014 /Published
online: 30 September 2014# Springer-Verlag Berlin Heidelberg
2014
Abstract Objective and unbiased validation studies over
asignificant number of cases are required to get a more
solidpicture on craniofacial superimposition reliability. It will
notbe possible to compare the performance of existing and up-coming
methods for craniofacial superimposition without acommon forensic
database available for the research commu-nity. Skullface overlay
is a key task within craniofacialsuperimposition that has a direct
influence on the subsequenttask devoted to evaluate the skullface
relationships. In thiswork, we present the procedure to create for
the first time sucha dataset. We have also created a database with
19 skullfaceoverlay cases for which we are trying to overcome legal
issuesthat allow us to make it public. The quantitative analysis
madein the segmentation and registration stages, together with
thevisual assessment of the 19 face-to-face overlays, allows us
toconclude that the results can be considered as a gold
standard.With such a ground truth dataset, a new horizon is opened
forthe development of new automatic methods whose perfor-mance
could be now objectively measured and comparedagainst previous and
future proposals. Additionally, other usesare expected to be
explored to better understand the visualevaluation process of
craniofacial relationships in craniofacial
identification. It could be very useful also as a starting
pointfor further studies on the prediction of the resulting
facialmorphology after corrective or reconstructive
interventionismin maxillofacial surgery.
Keywords Forensic anthropology . Craniofacialsuperimposition .
Computer-aided craniofacialsuperimposition . Skullface overlay .
Ground truth .
Craniofacial relationships
Introduction
Anthropologists have focused their attention on determiningthe
identity of a missing person when skeletal informationbecomes the
last resort for the forensic assessment [1, 2].Craniofacial
superimposition (CFS) [3], one of the approachesin craniofacial
identification [4, 5], involves the superimposi-tion of a skull (or
a skull model) with a number of antemortemimages of an individual
and the analysis of their morpholog-ical correspondence.
Regardless of the technological means considered, we
dis-tinguished three different stages for the whole CFS process
in[6]: (i) the first stage involves the acquisition and
processingof the skull (or skull 3D model) and the antemortem
facialimages together with the craniometric and facial
landmarklocation, (ii) the second stage is the skullface
overlay(SFO), which focuses on achieving the best possible
superim-position of the skull and a single antemortem image of
themissing person. This process is repeated for each
availablephotograph, obtaining different overlays. SFO thus
corre-sponds to what traditionally has been known as the
adjustmentof the skull size and its orientation with respect to the
facialphotograph [3, 7], and (iii) the third stage accomplishes
thedecision-making. Based on the superimpositions achieved inthe
latter SFO stage, the degree of support of being the same
O. Ibez (*) :C. Campomanes-lvarezDepartment of Computer Science
and Artificial Intelligence,University of Granada, 18014 Granada,
Spaine-mail: [email protected]
O. Ibez :B. R. Campomanes-lvarez :A. ValsecchiEuropean Centre
for Soft Computing, 33600 Mieres, Asturias, Spain
F. CavalliResearch Unit of Paleoradiology and Allied Sciences,
OspedaliRiuniti di Trieste, Trieste, Italy
M. I. HuetePhysical Anthropology Laboratory, University of
Granada,18012 Granada, Spain
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person or not (exclusion) is determined by considering
thedifferent factors studying the relationship between the skulland
the face: the morphological correlation, the matchingbetween the
corresponding landmarks according to the softtissue depth and the
consistency between asymmetries.
Since the first documented use of CFS for identificationpurposes
[8], the technique has been under continuous devel-opment. Although
the foundations of the CFS method werelaid by the end of the
ninetieth century [9, 10], the associatedprocedures evolved as new
technology was found available.Therefore, three main different
approaches have been devel-oped: photographic, video, and
computer-aided superimposi-tion [3, 6, 11].
The first superimpositions involved acquiring the negativeof the
original facial photograph and marking the facial land-marks on it.
The same task was done with a photograph of theskull. Then, both
negatives were overlapped and the positivewas developed. This
procedure was called photographic su-perimposition [3]. Many
authors further developed photo-graphic superimposition techniques
to improve the scale andthe orientation of the skull and the facial
images [1214].
Video superimposition was introduced in 1976 [15].Instead of
marking photographs, tracings, or drawings in orderto properly
superimpose the skull and the face, video camerasprovide a live
image of the object (skull, photograph) fo-cused. These systems
present an enormous advantage over theformer photographic
superimposition procedure by minimiz-ing several problems
associated to it. The video superimposi-tion technique continued
evolving [1618], and it became themost broadly employed method.
The popularization, huge development, and larger amountof
possibilities offered by computers turned them into the
nextgeneration of CFS systems. Two different system categoriesarise
within this group [6]. Nonautomatic computer-aidedmethods use the
computer for storing and/or visualizing thedata [7, 19, 20, 11],
but they do not consider the computationalcapacity to automate
human tasks. Automatic computer-aidedmethods use a computer program
to accomplish any CFSsubtask itself [21, 22].
Computer-aided methods are attracting more attention ofboth
practitioners and researchers [23]. In general, they areconsidered
the most promising approaches and in particular,automatic methods
represent the most appropriate tool toincrease the objectivity and
reliability of the CFS technique.
Numerous factors have an impact at the various stages ofCFS
method and can potentially introduce biases and affectthe outcome
and reliability of the superimposition. Many ofthe difficulties can
be tackled with computer-aided automaticsolutions, enhancing CFS
reliability. For many cases, the onlyavailable photograph is
distorted or has poor quality, computervision algorithms can be
easily implemented to enhance thequality of photographs. Modern
superimposition techniquesaside from high-quality photographs also
require accurate 3D
models of the skull which can be acquired from medicaltechnology
equipment such as a CT scan or laser range scan-ner in combination
with appropriate software [6].
The most time-consuming and challenging step in CFS
ispositioning of the skull to match the orientation and pose seenin
a target photograph [16, 24]. To perform the overlay of theface and
skull, most methods rely on thematching of a numberof cranial and
facial landmarks. For nonautomatic systems,either photo, video or
computer based, this process is usuallyslow and conducted by trial
and error. Adjusting the size andorienting the images can take
hours to arrive at a good overlay.The works developed by authors
such as [22, 2527] serve asexamples of how computer algorithms can
automate SFO andaccommodate the uncertainty/fuzziness of some
facial land-mark [28], improving CFS reliability by reducing
subjectivityand time inherent to nonautomated methods. The success
ofthe final identification strongly relies on an accurate
superim-position since this is the previous step to analyze the
anatom-ical correspondence between the face and the skull.
Thus,reaching an accurate overlay is of paramount importancebefore
continuing with the final decision-making stage.However, there is
no single objective and reliable method inthe literature to
determine whether the achieved superimposi-tion is correct or not.
This is because we are trying to overlaytwo different objects (a
skull and a face), and this itselfintroduces an inherent
uncertainty [26].
The latter fact affects both nonautomatic and automaticmethods
independently of the technological means employed(photo, video, or
computer). In the case of computer-aidedautomatic methods, all
existing SFO approaches are evaluatedusing the distance between a
pair of corresponding landmarks[21, 22, 2527]. This is clearly an
unsatisfactory evaluationsince it does not consider neither the
depth of the soft tissuenor the morphological matching of the face
and the skull.Thus, visual evaluation represents the only
meaningful avail-able resource, despite being a subjective and
expert-dependentprocedure.
In the current contribution, we aim to address this impor-tant
shortage. We have created a ground truth dataset whichwill allow to
measure and compare the performance of auto-matic SFO methods
following an objective and reliable pro-cedure to assess the SFO
achieved. With such a ground truthdataset, a new horizon is opened
for the development of novelautomatic methods whose performance
could be now objec-tively measured and compared against previous
and futureproposals.
Material and methods
With the goal of achieving a number of ground truth SFOs,frontal
and lateral photographs were taken from patients whosehead has just
been scanned with a cone beam computed
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tomography (CBCT) (further details about the data acquisitionare
provided in subsection Data acquisition). The DICOMimages resulting
from the CBCT machine were automaticallyprocessed to obtain the
corresponding 3D face and 3D skullmodels (see subsection Automatic
segmentation of cone beamcomputed tomography data for a detailed
description of thesegmentation algorithm). After positioning
homologous pointsin both the 3D face model and the photograph, the
former wasautomatically projected onto the latter so they perfectly
match(the fundamentals and method followed for this task are
de-scribed in subsection Image registration process). Then,
theparameters originating that perfect match between the 3D
facemodel and the photograph were applied to the 3D skull
model,resulting in a perfect SFO. The latter should be the ground
truthprojection of the skull over the face photograph. Thus, for
eachcase, we record the 2D location (x and y pixels) of
somelandmarks previously marked on the 3D skull model as theground
truth data to compare with. Figure 1 graphically showsan overview
of the whole ground truth data creation process.Finally, in order
to be able to validate the ground truth datasetusing real distances
(in millimeters), a method to estimatedistances in the 3D space is
applied between 3D facial pointsand 2D facial points backprojected
using the registration trans-formation (see subsection Real
distances estimation).
Data acquisition
The subjects were submitted to CBCT for clinical purposes.During
the same clinical session, the patient was undergoing
CBCT and subsequently, some photographs for clinical
doc-umentation were taken in the two orthogonal projections(front
and side) and, in one case, in oblique projection. Thepatient was
asked to maintain a neutral expression. The pho-tographs were taken
at a distance between 1 and 1.5 m, using adigital camera with a CCD
with a minimum resolution of4 Mpx. The patients expressed their
informed consent to theuse of their clinical data, anonymized, for
study and researchpurposes.
The data was acquired in multiple locations. Thus, acqui-sitions
were obtained with different equipment but with min-imum
requirements of acquisition characteristics
(orthostatic,acquisition time 1515 mm, voxel 0.30.30.3 mm).
Nine different persons, without facial disorders, werescanned in
total. For eight of them, two photographs, frontaland lateral
views, were taken. In one case, the one showed inFigs. 6 and 7,
three photographs, frontal, lateral, and obliqueviews, were
taken.
Automatic segmentation of cone beam computed tomographydata
In CT scans, the grey level of a tissue is a function of
itsradiodensity, usually measured in Hounsfield Units (HU).This
means that by selecting the voxels having grey valuesin a specific
range, one can easily separate regions of theimage having different
radiodensities such as air, soft, andbony tissues. In CBCT,
however, the relation between grey
Fig. 1 Overview of the groundtruth data creation process
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level and radiodensity is inaccurate so that regions having
thesame density appear with different grey values, depending
ontheir position relative to the organ being scanned [2931].
Segmentation of the face surface: the difference inradiodensity
between the head and the surrounding air volumeis quite large.
Voxels corresponding to air have values be-tween 1,000 and 800 HU,
while the head volume has aradiodensity above 400 HU. Thus, despite
the accuracy issuewe havementioned earlier, the head volume can be
segmentedby simply selecting the voxels having HU higher than
athreshold. The actual threshold values used in each imagewere
found manually but the process is quick and straightfor-ward
nevertheless. The face surface is then created by com-puting the
polygonal mesh surrounding the head volume. Thisstep was performed
using the well-known marching cubesalgorithm followed by smoothing
and decimation [32]. Boththe segmentation and the mesh computation
were performedusing the Slicer software package [33].
Segmentation of the skull surface: the grey level
valuesassociated with soft and bone tissues have a significant
over-lap (see Fig. 2). Therefore, the simple approach used
tosegment the head volume cannot be applied. In addition,CBCTshows
significant levels of noise and artifacts with greylevels in a
similar range to that of bones.
The grey level alone is not a reliable indicator of a
voxelbelonging to a bone. To overcome this issue, our
methodconsiders the texture of the image, i.e., the patterns of
greylevels that occur between a voxel and the surroundingones. We
used the well-known approach introduced byHaralik in [34] for
texture classification. For each voxelv, one computes the
distribution of grey level that occursbetween v and their
neighbors. This results in a co-occurrence matrix from which a
series of numerical tex-ture descriptors are computed, each
measuring a certaincharacteristic of the texture of the image
around v. Forinstance, texture correlation measures the degree of
depen-dence between the grey values of neighboring voxels,resulting
in high values when the grey levels in an areahave a regular or
gradual variation and in small valueswhen the grey level changes
suddenly or in an irregular
fashion. We considered three descriptors, namely energy,inertia,
and correlation [34].
Texture descriptors are able to characterize the appearanceof a
tissue to a much larger extent than just the grey level.However, to
segment the skull, we need to find a criterion totell apart bony
and non-bony voxels based on their grey leveland texture
descriptors. Instead of attempting to design thecriterion manually,
we adopted a classic machine learning(ML) [35] approach, in which
the solution of the problem is*learned* from a series of examples.
In ML, such criterion isknown as *classifier*, as it provides a way
to classify someobjects (in this case, the texture descriptor
values of a specificvoxel) into a number of classes (bony or
non-bony tissue). A*learner*, instead, is the kind of algorithm
that creates aclassifier automatically from a set of examples, in
this case,a set of already classified voxels.
Among several well-known classifiers, we choose decisiontrees
[36], which are graphical tree-like models representing
adecision-making process. Each internal node of the tree
rep-resents a condition C. For every possible outcome of C, thereis
a branch leading to another condition or to a leaf node,which
indicates a class. To classify an instance of the problemX, one
begins at the top of the tree, evaluates the conditions,and takes
the associated branch until a leaf node is reached;the class of X
is that of the terminal node. In our specific case,each decision is
the value of a texture feature being greater orsmaller than a given
threshold so the possible outcomes arejust true or false.
The process of learning a classifier is shown in Fig. 3.
Itbegins with the creation of a set of examples, i.e., a set
ofobjects that have been already classified, usually by an
expert.In this case, we used a CBCT scan that has been
manuallysegmented by a specialist (Dr. Cavalli) and computed its
texturedescriptors. The dataset was segmentedmanually, after a
coarseautomatic threshold-based segmentation, with the aim to
elim-inate residual noise artifacts. The CBCT dataset was
processedwith a multipurpose software for medical/scientific
imaging(Amira, Visage Imaging Inc.), and the segmentation
wasexecuted manually slice by slice in orthogonal projections
withits specific tool to obtain a clean skull image.
Fig. 2 The histogram showingthe grey level of bony and non-bony
tissues, depicted in grey andblack, respectively. Note the
largeoverlapping region
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Each voxel provides an example of the associationbetween the
values of the texture descriptors of a voxeland the corresponding
status of belonging to a bony tissueor not. We created a training
set by considering all voxelsof the manually segmented image. Then,
the training setwas fed to a decision tree learner, which computes
adecision tree to correctly classify the training data. Weused the
reference learning algorithm for decision trees,C4.5 [37],
implemented in the Weka machine learningtoolkit [38].
Once the decision tree has been created, a CBCT scan issegmented
by applying the classification process to all itsvoxels. This
results in a volume of voxels being marked asbone tissue. After
this process, the segmented volume isrefined by removing isolated
sets of voxels having a sizebelow a certain threshold. This removes
some of the voxelsbeing incorrectly classified as bone due to noise
and artifactsin the original image. Finally, a polygonal mesh is
createdfrom the segmented volume following the same procedureused
before in the face model generation. The overall proce-dure is
shown in Fig. 4.
Image registration process
The aim of this problem is to find a geometric transformation
fthat, when applied to the 3D face/skull model, can locate
itexactly in the same pose the patients head had in the momentof
the photograph. Thus, the problem of superimposing the3D face model
over the photograph is modeled following anImage Registration (IR)
[39] approach. The photograph istechnically the result of the 2D
projection of a real (3D) scenethat was acquired by a particular
(unknown) camera [40]. Insuch a scene, the person (the patient in
our case) was some-where inside the camera field of view with a
given pose. Thegoal is to replicate the latter original scenario.
Thus, thefollowing steps are performed by our method:
1. First, two sets of homologous points are located in boththe
3D face model and the photograph. In our case, thosepoints do not
necessarily correspond to somatometriclandmarks but in many cases,
they are just points thatcan be easily and accurately located in
the 3D model andthe photograph.
Fig. 3 An overview of thecreation of the classifier used inthe
proposed segmentationmethod
Fig. 4 The process ofsegmenting the skull in a CBCTusing a
classifier
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2. The face 3D model is positioned in the camera
coordinatesystem through geometric transformations, i.e.,
transla-tion, rotation, and scaling.
3. Then, a perspective projection of the 3D skull model ontothe
2D photograph of the face is performed (the perspec-tive projection
is related with the camera focal distanceand the LCD matrix
size).
4. Points two and three of this process are iteratively
per-formed, using different transformations, a number of
iter-ations given a priori (see Fig. 5 for an overview of
theprocess).
Therefore, the described framework involves a 3D2D IRtask
compiling geometric transformations and a perspectiveprojection
modeled by 12 unknown parameters [25]. Anautomatic method, in our
case, a genetic algorithm [41],iteratively searches for a
transformation (the values of thelatter unknown parameters) that
minimizes the distancesamong the corresponding landmark pairs. The
interested read-er is referred to [25] which provides a detailed
description ofthe optimizer together with the mathematical
structures usedfor the geometric transformations and the
perspectiveprojection.
The distance between homologous points (see Tables 2and 3) in
the superimposed images is considered to ob-jectively validate the
ground truth overlay. Once the 3Dface model is perfectly
superimposed over the photo-graph of the face, we can directly
apply the same geo-metric transformation over the 3D skull model to
perfectlysuperimpose it in the same manner. The final
transforma-tion, the whole picture of the skull projected in the
2Dimage or the location of a set of craniometric landmarks,can be
used as ground truth data for comparison andmethod validation
purposes.
Real distances estimation
The Euclidean distance between homologous points (seeTable 2) in
the superimposed images is considered to objec-tively validate the
ground truth overlay. As we are measuringdistances in an image (2D
plane), they are given in pixels.
Besides the error in pixels, we have included an
additionalestimation of the total error in millimeters (mm).
Bybackprojecting the facial points located in the photograph,we can
calculate a backprojection ray for a given geometrictransformation
f. Thus, we apply the inverse, f1, of the sametransformation f we
want to validate and then we choose onepoint of this ray as the 3D
position of the 2D point. Inparticular, we select the point that
makes minimum theEuclidean distance between the ray and the facial
point inthe 3D model.
More formally, the geometric transformation f iscalculated
following the explanation in [25], F=C (A D1 D2 D2
1D11A1) S T P, where F and C are the
corresponding sets of 2D facial and 3D facial
points,respectively. However, in order to backproject the 2Dfacial
points, we use the inverse of that equation asfollows: C=F [(A D1D2
D2
1 D11A1) S T P]1
Then, we obtain two different points, C0 and Cb, of
thebackprojected ray by applying this last equation twiceusing
different values for the z coordinate of matrix F,z=0 and z=b, b
being any constant value. As a result,with these two points, we
formulate the equation of the
backprojected ray as follows: r! r0! t v! < xC0 ;yC0 ; zC0
> t < xCbxC0 ; yCbyC0 ; zCbzC0
Finally, as already explained, the estimation error (in mil-
limeters) is defined as the minimum Euclidean distance be-tween
a 3D facial point and the backprojected ray generatedby f1 from its
corresponding actual point in the photograph.Table 3 depicts the
individual and average estimation errors(in millimeters) for
corresponding 3D and 2D points in all thecases.
Results
Two different experimental results are provided in this
section:on the one hand, a quantitative analysis of the performance
ofour 3D skull segmentation method for CBCT data; on theother hand,
the validation of the ground truth data.
Segmentation method evaluation
A reliable way to assess the quality of an automatic
segmen-tation is to compare with that performed by an expert,
assum-ing this is completely or at least very accurate. When
suchground truth segmentation data is available, the automaticFig.
5 The IR optimization process
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segmentation can be evaluated quantitatively by some mea-sure of
the agreement between automatic and manual results,such as the Dice
coefficient [34].
In this study, however, no such data is available. Twoimages
have been manually segmented by an expert, but thesegmentation is
not accurate enough to be considered groundtruth. Indeed, the
segmentations were actually performed withthe aim of creating a
mesh from the segmented volumes,rather than accurately mark the
bony tissue in the images.This means that, for instance, while the
exterior of the verte-brae has been segmented precisely, the
internal part was notmarked as bones, as the process of creating a
mesh onlyinvolves the outermost voxels of each structure.
Bearing this issue in mind, we have performed two kinds oftests
to assess the quality of our automatic segmentationtechnique. Let
us consider the two images having manualsegmentation, A and B, and
recall that one of such segmenta-tions is required in the process
of learning the classifier. First,we tested the accuracy of the
classifier learned over a sampleof an image to segment other data
from the same image. Thisresult shows whether the texture
descriptors and the kind ofclassifier employed are able to
characterize the status of atissue. Second, we tested the accuracy
of the classifier learnedon A to segment B and vice versa. This
indicates the ability ofthe approach to generalize the information
gathered on oneimage to another one, and in the end, it shows that
the methodis robust and it can perform properly regardless of the
actualimage used for learning the classifier.
While the second test is straightforward, the first test
em-ploys a standard technique for testing classifiers called
cross-validation (CV) [42]. The idea is to consider the set of
examples
created from the voxels of the image that have been
manuallysegmented. The set is split in two subsets, called training
andtesting sets. The former is used to train a decision tree, while
thesecond is used to evaluate it. The data is split at random in
nsubsets called folds. N-1 folds are used to train the
classifier,while the remaining fold is used for its test. The
process isrepeated N times, where each fold plays the role the test
data.Finally, the results of the classifier are averaged over theN
runs.
The results of the tests are reported in Table 1. We mea-sured
the accuracy of the classification, i.e., the percentage ofvoxels
that were classified correctly, which in this context isequivalent
to the Dice coefficient of the corresponding seg-mentations. The
results are excellent, with 94.7 % being thelowest value, and
describe a quite clear picture. From the firsttwo tests, one can
conclude that the texture descriptors areeffectively discriminating
the texture of bony tissues from thatof soft tissue and air.
Moreover, decision trees are actually ableto express the
relationship between texture descriptor valuesof bony tissues. This
validates the overall classification-basedapproach, and notably, it
happens despite the classifier hasbeen trained with imprecise
data.
Validation of the skullface overlay ground truth
In order to quantitatively and objectively assess the
groundtruth SFO data, we have to analyze the 3D2D face
overlaysemployed for its generation. Tables 2 and 3 show, for each
3Dface2D face overlay problem, the distance between corre-sponding
points in the final superimposition image. Mean andmaximum
distances are also reported. The distance used inevery case is the
Euclidean distance. While Table 2 shows
Fig. 7 Example of the groundtruth of three 3D skullfaceoverlays
corresponding to thethree cases showed in Fig. 6 (fromleft to
right, ID-5-F, ID-5-L, andID-5-O). Red dotswere employedto locate
some craniometriclandmarks on the 3D model
Fig. 6 Example of 3 out of the 193D face2D facesuperimpositions.
In particular,from left to right, ID-5-F, ID-5-L,and ID-5-O. Green
and red dotsrepresent the points used toregister the face model
over thephotograph
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Euc l i dean d i s t ance s measu r ed in p ixe l s , dp x1x2 2
y1y2 2
q, where (x1, y1) and (x2, y2) represent
the pixel coordinates of two points, Table 3 shows theEuclidean
distances measured in millimeters, dmm
x1x2 2 y1y2 2 z1z2 2q
, where (x1,y
1, z
1) and
(x2,y2,z2) represent 3D (real) coordinates of two points.
As Table 2 shows, the mean error ranges from 1.79 to4.38 pixels.
If we compare these error values with the totalarea the face has
into the pictures (it goes from around 450800 to 600950 pixels), we
can conclude that the error is notsignificant.
Similarly, Table 3 shows mean errors ranging from 0.06 to2.14
mm, and only in two cases, the mean distance is above1 mm. Although
real distances depicted here are estimationsand cannot be
considered as precise measurements, they con-firm the precision of
the matching between the 3D face modeland the facial photograph.
The sources of error and uncertain-ty affecting both pixel- and
millimeter-based measurementsare discussed in the final Discussion
and conclusionssection.
Together with the quantitative validation, we also per-form a
visual assessment of the resulting 3D face2D facematching. In case
of a significant difference on the artic-ulation of the mandible
and/or mouth between the modeland the photograph, the visual
assessment does not con-sider their region of influence. Visually
checking eachcase, we conclude that in all the cases, the 3D face
modelperfectly overlays the face in the photograph. Figure 6shows
three different overlays corresponding to ID-5-F,ID-5-L, and ID-5-O
cases. The analysis of these threecases can perfectly serve as an
example of the remainingones. In case ID-5-F, while the visual
assessment con-cluded a perfect matching (mouth area was not
subject forevaluation), the mean distance between landmarks is3
pixels, with a maximum distance of six. However, thesedistances are
observed to be not significant since all thehomologous points are
touching each other. In the remain-ing two cases (ID-5-L and
ID-5-O), the visual assessmentindicated a wrong matching in
mandible and mouth areas.These can be explained again by the
different apertures ofthe mouth. The overall matching is again
perfect, anevaluation supported by the insignificant mean
distancesbetween facial points (partially or completely
overlapped),2.22 and 1.84 pixels (0.50 and 0.08 mm),
respectively.The corresponding SFOs are depicted in Fig. 7.
Discussion and conclusions
Objective and unbiased validation studies over a
significantnumber of cases are required to get a more solid picture
onCFS reliability. It will not be possible to compare the
perfor-mance of existing and upcoming methods for CFS without
acommon forensic database available for the research commu-nity.
Skullface overlay is a key task within CFS that has adirect
influence on the subsequent task devoted to evaluate theskullface
relationships. In the present work, we present theprocedure to
create for the first time such a dataset. We havealso created a
database with 19 SFO cases for which we aretrying to overcome legal
issues that allow us to make it public.The quantitative analysis
made in the segmentation and reg-istration stages, together with
the visual assessment of the 19face-to-face overlays, allows us to
conclude that the resultscan be considered as a gold standard.
Within this study, we have preferred to employ CBCTrather than
multiple detector computed tomography (MDCT)for the following
reasons:
(a) In CBCT, it is possible to obtain a scan in
orthostaticposition, more physiological than clinostatic
acquisi-tion of MDCT where a certain degree of deformation ofthe
soft tissues takes place due to gravity [43]
(b) In CBCT, there is no systematic error comparing
averagehomologous landmark coordinates in conventional digi-tal
cephalograms and CBCT-generated cephalograms[44]
(c) The low exposure dose of CBCT [45], which makes thistype of
investigation very common in the clinical prac-tice. It must be
emphasized, however, that our subjectswere undergoing CBCT for
clinical purposes: to submit asubject to an X-ray examination,
albeit with very lowdose without a reasoned clinical need, clashes
with theprinciples of justification and ALARA [46]
The CBCT is an equipment made for the clinical study ofbone and
teeth rather than for the soft parts of the face.Therefore, it also
has some disadvantages: (i) the modeststatistic of the image with a
signal/noise ratio lower thanMDCT due to the low dose of
acquisition, (ii) the maximumfield of view (FOV), optimized for the
acquisition of the jawor malar, does not allow the acquisition of
the whole skulluntil the vertex, and (iii) the displayed grey
levels in CBCTsystems are arbitrary and do not allow for the
assessment ofbone quality as performed with Hounsfield Units (HU)
in
Table 1 Accuracy of the pro-posed segmentation method 10-fold
CVover A 10-fold CVover B Training A, test B Training B, test A
Accuracy/DICE 98.4 % 97.8 % 94.7 % 94.9 %
576 Int J Legal Med (2015) 129:569581
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Table2
Euclideandistance
betweencorrespondingpointsofthe3D
face
model(onceprojectedintothephotograph)and
thephotograph
oftheface.Inthefirstcolum
non
theleft,F
isused
forfrontalview
cases,Lforlateralviewcases,andOfortheoblique
view
case
Case
3D2Dface
pointm
atchingEuclideandistance
(pixels)
Max.dist.
Meandist.
ID1F
00
11
11.41
1.41
2.24
3.61
4.12
55
5.39
5.39
2.40
ID1L
02.83
2.83
2.83
3.16
3.16
44.12
4.47
56.08
6.40
6.40
7.07
7.28
7.28
4.38
ID2F
11.41
1.41
2.24
2.83
44.47
5.39
5.83
77.28
9.01
9.01
4.32
ID2L
00
1.41
45
5.39
6.71
8.25
8.25
3.84
ID3F
00
1.41
22.24
2.24
2.83
3.61
5.66
6.08
6.71
7.21
7.21
7.21
3.53
ID3L
00
1.41
2.24
2.24
2.24
33.16
3.16
1.79
ID4F
01
22.24
2.83
4.12
4.12
4.24
5.10
5.66
6.40
7.07
10
10
4.21
ID4L
00
01
12
29.90
9.90
1.99
ID5F
11
12.24
33.16
3.16
44.24
55
5.83
6.40
6.40
6.40
3.67
ID5L
00
12.83
33.16
3.16
3.16
3.61
3.61
2.21
ID5O
00
00
2.24
9.22
9.49
9.49
2.99
ID6F
01
1.41
1.41
23.16
4.12
55
5.39
5.83
5.83
3.12
ID6L
00
01
3.61
7.21
7.21
1.97
ID7F
00
11
2.24
2.24
2.83
33.16
59.22
9.22
2.69
ID7L
00
01.41
3.61
5.66
5.66
5.83
6.40
9.06
9.06
3.76
ID8F
00
11.41
22.24
2.24
2.24
2.83
46.71
6.71
2.24
ID8L
00
3.61
4.47
5.39
8.06
8.06
3.59
ID9F
01
11.41
1.41
22
2.83
3.61
3.61
3.61
3.61
2.04
ID9L
00
11
1.41
2.24
5.10
9
92.47
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Table3
Estimated
realdistance
inmillimetersbetweencorrespondingpointsof
the3D
face
model(onceprojectedintothephotograph)andthephotograph
oftheface.Inthefirstcolum
non
theleft,F
isused
forfrontalviewcases,Lforlateralviewcases,andOfortheoblique
view
case
Case
3D2Dface
pointm
atchingEuclideandistance
(mm)
Max.dist.
Meandist.
ID1F
0.00
0.00
0.04
0.06
0.12
0.14
0.14
0.15
0.15
0.20
0.24
0.24
0.28
0.28
0.14
ID1L
0.07
1.32
1.41
1.55
1.86
1.92
1.94
2.06
2.17
2.18
2.83
3.02
3.05
3.06
3.62
3.62
2.13
ID2F
0.04
0.04
0.06
0.07
0.08
0.08
0.09
0.10
0.11
1.22
2.15
3.16
3.16
0.56
ID2L
0.00
0.00
0.03
0.06
0.07
0.08
0.09
0.10
0.10
0.05
ID3F
0.00
0.00
0.02
0.10
0.10
0.12
0.13
0.13
0.15
0.16
0.83
0.98
1.08
1.53
1.53
0.38
ID3L
0.01
0.01
0.5
0.55
0.66
0.71
0.84
1.59
1.59
0.61
ID4F
0.04
0.05
0.05
0.05
0.06
0.07
0.07
0.08
0.08
0.09
0.09
0.09
0.09
0.09
0.07
ID4L
0.00
0.00
0.03
0.05
0.09
0.09
0.11
0.13
0.13
0.06
ID5F
0.10
0.19
0.21
0.37
0.39
0.46
0.50
0.52
0.53
0.55
0.58
0.65
1.45
1.69
1.69
0.58
ID5L
0.00
0.07
0.21
0.29
0.30
0.30
0.45
1.23
1.63
1.63
0.50
ID5O
0.00
0.01
0.01
0.09
0.13
0.14
0.17
0.17
0.08
ID6F
0.00
0.28
0.31
0.34
0.42
0.59
0.91
0.91
1.24
1.39
2.24
2.24
0.78
ID6L
0.00
0.00
0.00
0.79
1.04
1.72
1.72
0.59
ID7F
0.01
0.04
0.05
0.06
0.06
0.11
0.11
0.11
0.16
0.18
0.18
0.09
ID7L
0.00
0.00
0.29
0.59
1.00
1.52
1.62
1.72
2.43
6.40
6.40
1.56
ID8F
0.00
0.00
0.30
0.49
0.58
0.60
0.62
0.65
0.70
0.99
1.86
1.86
0.62
ID8L
0.00
0.00
0.07
0.07
0.10
0.10
0.10
0.06
ID9F
0.02
0.04
0.06
0.08
0.08
0.09
0.12
0.12
0.15
0.15
0.17
0.17
0.10
ID9L
0.00
0.00
0.03
0.08
0.09
0.10
0.11
0.14
0.16
0.16
0.08
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medical CT. In [47], the authors demonstrated that it should
bepossible to derive one from the other. However, the
practicalapplication of this method is almost lacking. As the
authorspointed out, the results were obtained in an ideal situation
inwhich the location, the size of each material in the 3D
dentalphantom, and the subsequent size ROI to sample were
known.Additionally, at low kilovolt and milliampere settings
onCBCTmachines, quantum noise may be sufficient to interferewith
estimation of the actual grey level. Another assumptionof that
study is that the photon beam in CBCTmachines obeysthe laws of
narrow beam attenuation. Contrary to the assump-tion, CBCT machines
operate with an area detector which isnot collimated like fan beam
medical CT. In conclusion, thereis a need of ad hoc CBCT
segmentation methods.
Thus, we designed, implemented, and tested a new novelmethod for
skull segmentation in CBCT data using decisiontree classifiers and
texture information. This method allows usto have automatic,
accurate, unbiased, and expert-independent3D skull segmentation
from CBCT data.
These medical data of the head allow us to obtain a reliableand
accurate ground truth. As introduced before, this is pos-sible
thanks to the presence of the corresponding 3D facemodel together
with the skull. The 3D face model gives usthe possibility to
superimpose a face (3D model) over thesame face (photograph), i.e.,
superimpose the same objectacquired by different sensors. The
process of finding a geo-metric transformation that overlays two
images taken underdifferent conditions (at different times, from
different view-points, and/or by different sensors) is called image
registration(IR) [39]. Several works reviewing the state of the art
on IRmethods have been contributed in the last few years
[4850].Despite an extensive survey on every aspect related to the
IRframework that is out of the aim of this work, we would like
tobriefly describe the key concepts concerning the IR method-ology
in order to achieve a better understanding of our work.There is not
a universal design for a hypothetical IR methodthat could be
applied to all registration tasks since variousconsiderations on
the particular application must be taken intoaccount. Nevertheless,
IR methods usually require the fourfollowing components (see Fig.
5): two input images namedscene and model; a Registration
transformation f, being aparametric function relating the two
images; a Similaritymetric, in order to measure a qualitative value
of closenessor degree of fitting between the transformed scene
image andthe model image; and an Optimizer which looks for
theoptimal transformation f inside the defined solution
searchspace. The case of SFO is a complex task because we aretrying
to reproduce the original scenario with an importantnumber of
unknowns coming from two different sources [25]:(i) the camera
configuration: at the moment of the acquisition,there were
different parameters that have an influence in theSFO problem. In
particular, the focal length or the distancefrom the camera to the
person; and (ii) the 3D face model: this
face model will have a specific orientation, resolution, andsize
given by the technical features of the considered scanner.Hence, a
3D2D IR process where all these unknown param-eters have to be
estimated seems to be the most natural andappropriate
formulation.
This technical procedure followed is the best scientificapproach
to the problem, it is quite robust and accurate, andit is automatic
and expert-independent (unbiased). Thus, itperfectly serves a
general procedure to develop ground truthdate sets with different
medical data (CBCT but also CT).However, the resulting ground truth
dataset we have generatedstill present some limitations:
(1) CBCT data have one major limitation, the reduced fieldof
view. In our case, all our 3D scans lack an importantpart of the
head, the upper part. This has a negativeinfluence on the
reliability of the facial superimpositionscarried out. Although we
have quantitatively and quali-tatively demonstrated the accurate
matching in the vali-dation section, the absence of a part of the
face modelmakes it impossible to conclude that a perfect
matchinghave been achieved. Similarly, if this dataset is
employedfor the comparison of SFO methods, conclusions of
howdifferent methods match the upper part cannot beachieved. This
is an important part since many practi-tioners rely on the cranial
outline significantly duringforensic practice and thus it will make
the SFO processmore difficult. As a result, there could be
methodsperforming badly in these conditions while those
couldperform better in a more realistic situation where all
thecranial data is included within the 3D skull model
(2) The validation of the CBCT segmentation method didnot make
use of ground truth data. From the two testsexplained in the
Segmentation method evaluation sub-section, it follows that the
results are highly consistentwith the manual segmentations. Note
that even a perfectsegmentation would have resulted in some error,
as weare not comparing with ground truth data, so the auto-matic
segmentations could actually be better than themanual ones. Also,
the actual image used for learningthe classifier had a negligible
effect on the final results,indicating the robustness of the
approach
(3) The correspondence among pairs of facial points in
thephotograph and in the 3D face model is not perfect. In anideal
situation, the distance between points should bealways zero.
However, in the real situation, we have totake into account two
different sources of error that arealmost impossible to
overcome:
a. Facial point location error: this is related to theextremely
difficult task to locate the points in theexact same place (pixel)
in both the 3D model andthe photograph
Int J Legal Med (2015) 129:569581 579
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b. Facial pose error: it is really difficult, if not
impossi-ble, that the patient has the same pose (mandible,mouth and
eye aperture) in the two different acqui-sition moments (scanning
and photographing). Inaddition, some of them were asked to smile
for thephotograph (to be able to evaluate teeth matching inCFS).
Thus, small facial changes should be assumed.Additionally,
homologous points were located inface regions where this effect is
expected to beinsignificant. For example, in most of the cases,
weavoided to locate points in the mandible area of theface to
reduce the effect of the facial pose error
Thus, the errors depicted in Tables 2 and 3 can be assumeddue to
the two different sources of error described before. Infact, these
mean and max error values are in any case ex-plained by the facial
landmark positioning error studies [28,51]. Notice that it is not
possible to calculate the precise 3Dposition of a 2D point in a
single image since the depthinformation (z coordinate) is unknown.
Nevertheless, we em-ploy a convention, the minimum Euclidean
distance betweena 3D facial point and the backprojected ray that
allows us toestimate distances in millimeter with the clear
advantage ofbeing independent of the image resolution.
As a result of the methodology proposed, once the 3D facemodel
is perfectly superimposed over the photograph of theface, we can
directly apply the same geometric transformationover the 3D skull
model to perfectly superimpose it in thesame manner. Thus, the
geometric transformation, the wholepicture of the skull projected
in the 2D image or the location ofa set of craniometric landmarks
can be used as ground truthdata for comparison and method
validation purposes.
Concerning the utility of these ground truth dataset or
thetechnical process described to generate new ones, there are afew
potential applications apart from the direct practical usefor
automatic SFO method assessment and comparison. Itcould be really
useful for studying the discriminative powerof the different
criteria for assessing the skullface relation-ship. In fact, this
represents the first use of the database in theframework of the
European project The new methodologiesand protocols of forensic
identification by craniofacial super-imposition (MEPROCS).1 A
subset of the 19 ground truthSFOs, together with an equivalent
number of manually ob-tained SFOs of negative cases, was provided
to several par-ticipants that were asked to evaluate a number of
morpholog-ical criteria for each case. Using the results obtained
from thisstudy, MEPROCS partners are trying to establish a raking
ofimportance of morphological criteria for CFS. This study, ofmain
forensic interest, may also have an important outcome inthe field
of maxillofacial surgery and orthodontics, not only toimprove our
knowledge of craniofacial relationships but also
as a starting point for further studies on the prediction of
theresulting facial morphology after corrective or
reconstructiveinterventions [52].
Acknowledgments Wewould like to thank all the participants that
giveus the permission to work with both their head scans and facial
photo-graphs, Drs. Luca Contardo and Domenico Dalessandri for the
supportprovided during images acquisition and head scanning. The
UniversityHospital of Trieste and Ortoscan for supporting this
research. This workhas been supported by the Spanish Ministerio de
Economa yCompetitividad under the SOCOVIFI2 project (refs.
TIN2012-38525-C01/C02, http://www.softcomputing.es/socovifi/), the
AndalusianDepartment of Innovacin, Ciencia y Empresa under project
TIC2011-7745, the Principality of Asturias Government under the
project withreference CT13-55, and the European Unions Seventh
FrameworkProgramme for research technological development and
demonstrationunder the MEPROCS project (Grant Agreement No.
285624), includingEuropean Development Regional Funds (EDRF). Mrs.
C. Campomanes-lvarezs work has been supported by Spanish MECD FPU
grant AP-2012-4285. Dr. Ibaezs work has been supported by Spanish
MINECOJuan de la Cierva Fellowship JCI-2012-15359.
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Ground truth data generation for skullface
overlayAbstractIntroductionMaterial and methodsData
acquisitionAutomatic segmentation of cone beam computed tomography
dataImage registration processReal distances estimation
ResultsSegmentation method evaluationValidation of the skullface
overlay ground truth
Discussion and conclusionsReferences