Data pre-









Spectral Mesh Processing for Craniofacial Dysmorphology

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This project addresses the analysis of facial geometry for the quantification of craniofacial Dysmorphology, motivated by: 1) its association with, and ability to inform on, diseases of early brain development, such as Down syndrome, fetal alcohol syndrome and schizophrenia; 2) increasing availability of three-dimensional (3D) imaging technologies that overcome many of the limitations inherent to two-dimensional approaches.

We focus on the development of algorithms for automated and highly accurate analysis of facial surfaces in 3D, with special interest in techniques based on spectral decomposition methods. As opposed to traditional methods, based on a reduced set of landmark points, spectral mesh processing (SMP) allows analysis of the whole facial surface. Briefly speaking, SMP algorithms provide a decomposition of the geometry into its natural vibration modes. The resulting components, analogous to the Fourier Transform for 1D signals, are linked to intrinsic properties of the object, such as (a)symmetry, believed to be a crucial component of dysmorphology. While SMP is a novel and very active trend in computer graphics and vision, it still involves a number of important technical challenges for its use in engineering applications, where input data would usually need to undergo one or more pre-processing steps, often with the need for human intervention before such spectral methods can be used.

The accuracy and precision of the algorithms used for geometric processing play a crucial role in the project given the interest in neuropsychiatric disorders, where craniofacial dysmorphology is considerably more subtle than, for example, in Down syndrome. We investigate the decomposition of 3D mesh geometries in general from a theoretical perspective, while taking advantage of the special characteristics of facial geometry (which allow, still with spectral methods, its mapping in 2D with very little distortion) to validate the 3D methods against results in the 2D domain, currently better understood.


Craniofacial Dysmorphology

The relation between craniofacial dysmorphology and certain diseases had been already suggested many years ago. Among the most evident examples are the distinctive facial characteristics of patients with Down syndrome [Ferrario 2005], but these have also been identified in autism [Ozgen 2010], schizophrenia [Hennessy 2007], bipolar disorder [Hennessy 2010], velocardiofacial syndrome [Óskarsdóttir 2005], fetal alcohol syndrome, etc. In the latter, the unique pattern of facial anomalies is the only diagnostic feature so far that is specific to the condition and has therefore been the focus of case definition [Mutsvangwa 2010].

New insights in developmental biology indicate a deep intimacy in morphogenesis of certain regions of the brain and the face, and a rapidly increasing number of genes have been identified as regulating cerebro-craniofacial development [Helms 2005]. As a concrete example, the forebrain, which acts as the supporting framework for facial morphogenesis, has been shown to provide signals with instructional information for elaborating the proximodistal and mediolateral axes of the middle and upper facial skeleton [Marcucio 2005]. This explains why disruptions to the early development of certain regions of the brain are accompanied by craniofacial dysmorphology.

Based on the above evidence, craniofacial geometry has been suggested as a potential index of early developmental disturbance [Hammond 2007, Hennessy 2010, Chakravarty 2011]. Recent technological advances on 3D imaging have made it possible to analyze craniofacial shape based on indirect measurements, as opposed to the classical direct anthropometry. However, in contrast to the evident dysmorphology in diseases like Down syndrome, dysmorphology in other disorders such as schizophrenia, bipolar disorder and velocardiofacial syndrome, can be very subtle to the extent that it can hardly be identified by the human eye. Therefore, we addressed the issues of highly accurate and fully automatic analysis of craniofacial geometry, with a special emphasis on the repeatability of results, so that large populations could be analyzed in a systematic and consistent manner.

The activity in this project can be roughly subdivided into 4 lines that have advanced in parallel:

·       Data pre-processing

·       Automatic landmark localization

·       Landmark-based surface normalization

·       Spectral mesh processing




[Chacravarty 2011] M.M. Chakravarty et al. Automated analysis of craniofacial morphology using magnetic resonance images.  Plos ONE 6(5):e20241, 2011.

[Ferrario 2005] V.F. Ferrario, C. Dellavia, G. Serrao and C. Sforza. Soft tissue facial angles in Down’s syndrome subjects: a three-dimensional non-invasive study. European Journal of Orthodontics 27(4):355–62, 2005

[Hammond 2007] P. Hammond. The use of 3D face shape modelling in dysmorphology. Archives of Disease in Childhood 92:1120–1126, 2007.

[Hennessy 2007] R.J. Hennessy, P.A. Baldwin, D.J. Browne, A. Kinsellac and J.L. Waddingtona. Three-dimensional laser surface imaging and geo- metric morphometrics resolve frontonasal dysmorphology in schizophrenia. Biological Psychiatry 61:1187–1194, 2007.

[Hennessy 2010] R.J. Hennessy, P.A. Baldwin, D.J. Browne, A. Kinsellac and J.L. Waddingtona. Frontonasal dysmorphology in bipolar disorder by 3D laser surface imaging and geometric morphometrics: Comparisons with schizophrenia. Schizophrenia Research, 122(1-3):63–71, 2010.

[Marcicio 2005] R.S. Marcucio, D.R. Cordero, D. Hu and J.A. Helms. Molecular interactions coordinating the development of the forebrain and face. Developmental Biology, 284:48–61, 2005.

[Mutsvangwa 2010] T.E.M. Mutsvangwa, E.M. Meintjes, D.L. Viljoen and T.S. Douglas. Morphometric analysis and classification of the facial phenotype associated with Fetal Alcohol Syndrome in 5- and 12-year-old children. American Journal of Medical Genetics Part A. 152A:32–41, 2010.

[Óskarsdóttir 2005] S. Óskarsdóttir, C. Persson, B.O. Eriksson and A. Fasth. Presenting phenotype in 100 children with the 22q11 deletion syndrome. European Journal of Pediatrics. 164(3):146–153, 2005.

[Ozgen 2010] H.M. Ozgen, J.W. Hop, J.J. Hox, F.A Beemer and H van Engeland,. Minor physical anomalies in autism: a meta-analysis. Molecular Psychiatry, 15:300–307, 2010.



The SP-MORPH project is funded by a Marie Curie Intra-European Fellowship (IEF) from the 7th Framework Program of the European Commission (Project Number 299605).


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