3D IMAGE PROCESSING FOR THE STUDY OF THE EVOLUTION
OF THE SHAPE OF THE HUMAN SKULL PRESENTATION OF THE TOOLS AND PRELIMINARY RESULTS


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INTRODUCTION
Computer Tomography Scan images are more and more used in paleo-anthropology (Spoor et al., 2000),
especially for the study of the craniofacial massif. The fossil, or its mold, is placed into a Computer Tomography device
(see Figure 1) and we obtain, in few minutes, a series of several tens of digital images that represent the successive slices
of the anatomical structure. These images are, in general, of a resolution of 512 by 512 pixels which are coded in several
thousands of gray levels. They are then “stacked” in order to build up a three-dimensional image. CT-Scan devices that
are routinely used in medical radiology have a resolution of one millimeter whereas special industrial micro-scanners
can reach up to a resolution of one hundred microns (Thompson and Ilerhaus, 1998).


PRESENTATION OF THE METHOD
Extraction of feature points and lines

To compute the 3D transformation, we have to find
some landmarks on the surface of the skull. They must be
defined by an unambiguous mathematical formula to be
automatically computed and be anatomically relevant to
characterize the structure. We choose crest lines (Thirion &
Gourdon, 1996), (Subsol et al., 1998) that are defined by
the extrema of the principal curvature that has the largest
absolute magnitude, along its associated principal direction
(see Figure 3). Due to their definition, these lines follow the
salient lines of a surface. We can verify this in Figure 4
where the crest lines that were automatically extracted in a
CT-Scan of the skull of a Modern Man emphasize the
mandible, the orbits, the cheekbones or the temples and
also, inside the cranium, the sphenoid and temporal bones
as well as the foramen magnum.


Computing the 3D transformation

Now, we have to compute the 3D transformation between the fossils. The Thin-Plate Spline method
(Bookstein, 1991), widely used in morphometry, allows to compute such a function that interpolates the displacements
of the homologous points (Pi,Qi) with some mathematical properties of regularity. Nevertheless, interpolation is relevant
when the matched points are totally reliable and distributed regularly (for example, with a few points being located
manually). In our case, these points are not totally reliable due to possible mismatches of the registration algorithm and
are sparse in a few compact areas as they belong to lines.


Facial reconstruction

In (Quatrehomme et al., 1997), we propose an automatic method to perform a 3D facial reconstruction based on
the 3D images of an unknown skull and of a reference skull and face (see Figure 10). We register automatically the 3D
images by using crest lines and we compute a 3D transformation between the two skulls. If we assume that the shape of
the face follows more or less the shape of the skull, we can apply this 3D deformation to the reference face and infer the
face corresponding to the unknown skull.


CONCLUSION
In this paper, we have presented several 3D image processing tools – feature extraction, feature registration,
complex deformation computation – that can be combined in order to compute and analyze the deformation between two
specimens. We have applied an entirely automatic methodology to the study of the shape of the skull of the Man of
Tautavel and we present some preliminary results. Even if they have not yet been compared to the current established
paleontology knowledge, we think that they are encouraging and assess the utility of such automatic tools, that are faster
than manual procedures, that give reproducible results, and that can be easily parameterized to allow the paleontologist
to test several hypotheses.