3D statistical facial reconstruction

The aim of craniofacial reconstruction is to produce a likeness of a face from the skull. Few works in computerized assisted facial reconstruction have been done in the past, due to poor machine performances and data availability, and major works are manually reconstructions. In this paper, we present an approach to build 3D statistical models of the skull and the face with soft tissues from the skull of one individual. Results on real data are presented and seem promising

Statistical skull models from 3D X-ray images

We present 2 statistical models of the skull and mandible built upon an elastic registration method of 3D meshes. The aim of this work is to relate degrees of freedom of skull anatomy, as static relations are of main interest for anthropology and legal medicine. Statistical models can effectively provide reconstructions together with statistical precision. In our applications, patient-specific meshes of the skull and the mandible are high-density meshes, extracted from 3D CT scans. All our patient-specific meshes are registrated in a subject-shared reference system using our 3D-to-3D elastic matching algorithm. Registration is based upon the minimization of a distance between the high density mesh and a shared low density mesh, defined on the vertexes, in a multi resolution approach. A Principal Component analysis is performed on the normalised registrated data to build a statistical linear model of the skull and mandible shape variation. The accuracy of the reconstruction is under the millimetre in the shape space (after rigid registration). Reconstruction errors for Scan data of tests individuals are below registration noise. To take in count the articulated aspect of the skull in our model, Kernel Principal Component Analysis is applied, extracting a non-linear parameter associated with mandible position, therefore building a statistical articulated 3D model of the skull.Comment: Proceedings of the Second International Conference on Reconstruction of Soft Facial Parts RSFP'200

The future of FEAST

"With the end of FEAST’s current contract on the horizon, and the fact that there are a number of exciting international issues in a state of flux or development, it is an appropriate time to report and assess FEAST’s achievements and what its suite of future services might be, and what would be a good model going forward. In December 2010 a group of FEAST’s stakeholders met in Canberra, at a workshop designed to examine FEAST’s role and achievements, and to discuss the current and emerging needs of the research community relevant to FEAST’s mission. This paper draws on those discussions, in addition to other dialogues between FEAST and its stakeholders, so that all interested individuals and organisations may provide comment on issues relating to the future of FEAST." - page 2Australian National Universit

New data model for graph-cut segmentation: application to automatic melanoma delineation

International audienceWe propose a new data model for graph-cut image segmentation, defined according to probabilities learned by a classification process. Unlike traditional graph-cut methods, the data model takes into account not only color but also texture and shape information. For melanoma images, we also introduce skin chromophore features and automatically derive "seed" pixels used to train the classifier from a coarse initial segmentation. On natural images, our method successfully segments objects having similar color but different texture. Its application to melanoma delineation compares favorably to manual delineation and related graph-cut segmentation methods