In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Voi. XXXVIII, Part 7B 
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ANALYSING THE FACIAL MORPHOLOGY WITH A THREE-DIMENSIONAL 
GEOMETRICAL FEATURES APPROACH 
F. Calignano a ’ ,S. Moos a , E. Vezzetti a * 
a Politecnico di Torino, Dipartimento di Sistemi di Produzione ed Economia dell’Azienda, 10129 Torino, Italy - 
(flaviana.calignano, sandro.moos, enrico.vezzetti)@polito.it 
KEY WORDS: 3D scanner, Shape analysis, Facial morphology, Soft tissue shifts 
ABSTRACT: 
To obtain the best surgical results in orthognathic surgery, treatment planning and the evaluation of results should be performed. In 
these operations it is necessary to provide to the physicians powerful tools able to underline the behaviour of soft tissue. For this 
reason, considering the improvements provided by the use of 3D scanners, as photogrammetry, in the medical diagnosis this paper 
proposes a methodology for analysing the facial morphology working with geometrical features. The methodology has been tested 
over patients affected by malocclusion, in order to analyse the reliability and efficiency of the provided diagnostic results. 
1. INTRODUCTION 
The assessment of the dimensions and arrangement of facial 
soft tissues is important for medical evaluations. Orthodontists 
and orthognathic maxillofacial and plastic surgeons often 
require quantitative data about the association between soft and 
hard tissues (Ferrario 1999, Sforza 2005). For many years, this 
information has been obtained from two-dimensional (2D) 
radiographs and photos, even if these have been consistently 
limited (Chew 2005,Koh 2004). Significant improvements have 
been obtained with the use of computer vision algorithms, even 
if the use of 2D supports to analyze 3D objects seems to be 
quite inadequate. For this reason, many research efforts over the 
last 10 years have been directed toward developing computer 
vision tools that with the use of 3D scanner devices are able to 
provide reliable and more complete data. These systems use 
different technologies such as active or passive light reflection 
analysis and are able to describe 3D real shapes with a point 
cloud, analyzable with 3D software. However, while image- 
processing methodologies are well known in the medical 
context, the situation for 3D scanners is still quite marginal and 
fragmented. Some studies have proposed structured procedures 
that could be used for guiding physicians in the use of 3D 
scanners in medical diagnosis (Hoffman 2005, Katsumata 
2005), but at present no one has succeeded in developing a 
standardized and accepted strategy. 
Actually, it is possible to move from morphometric tools that 
implement statistical shape analysis, such as Generalised 
Procrustes Superimposition (GPS) and Principal Component 
Analysis (PCA). The first iterative method (GPS) applies 
geometrical transformations (scales, translations, rotations, and 
reflections) in order to compare reference points (landmarks) 
(Mori 2005, Sforza 2004) taken from different point clouds of 
the patient’s face. The PCA method evaluates the tendency of 
the landmark distributions along the x and y axes, locating a 
new working frame centered on the average shape center. The 
method creates new variables named principal components 
(PCs) that describe how much the landmark configuration of 
each sample is different from the average shape. 
Moving to the 2D radiographs, Thin-Plate Spline analysis (TPS) 
is used on a point set of anatomical landmarks over the pre- and 
post-surgery radiograph. Then the postsurgery radiograph is 
considered as an infinitely thin metal plate that must be bent in 
a direction orthogonal to the plane in order to match its 
landmarks to those of the presurgery radiograph while the 
bending energy is minimized (Hajeer 2004) If the two shapes 
are identical, the bending energy is zero and the plate is flat. 
To provide information regarding face morphology in the 
regions around the landmarks, the Multisectional Spline method 
uses section planes passing through a set of specific reference 
points of a point cloud (landmarks) in order to obtain specific 
section spline. The shifts of the facial morphology between the 
pre- and post-surgery point clouds can be analyzed by 
comparing the two section profiles passing through homologous 
landmarks and section planes (Soncul 2004). 
Working with the entire point cloud instead of only some 
portions, with the Clearance Vector Mapping method CVM) the 
pre- and post-surgery point clouds are first aligned (iterated 
closest point (ICP), CSM) (McIntyre 2003) and then the 
magnitude of the 3D shape displacement can be computed by 
working on triangulated meshes, following different approaches 
(radial, normal) (Harmon 1981). The displacement is shown by 
color mapping. 
At present, even if the most used methodology for maxillofacial 
diagnosis remains the Conventional Cephalometric Analysis 
(CCA) (Bookstein 1991), the Multisectional Splines method 
seems to be the most reliable and complete methodology 
because it is able to provide reliable information about the 
tissue shifts, as does the CCA approach, but it is also able to 
provide additional global information, e.g., some pathologies 
such as lateral asymmetry. 
Some significant points need work for development of a 
diagnostic procedure that could be accepted by the entire 
medical context. It is necessary to define a method that extracts 
shape morphology measures, starting from the landmarks as 
reference points, to guarantee consistent morphological 
comparison, but that also takes into consideration the entire 
facial shape (point cloud) in order to consider all useful 
* E. Vezzetti, Dipartimento di Sistemi di Produzione ed Economia dell’Azienda, Politecnico di Torino, Corso Duca degli Abruzzi 
24, 10129 Torino, Italy, email: enrico.vezzetti@polito.it