Automatically building morphometric anatomical atlases from 3D medical images : Application to a skull atlas

In this article, we present a method for building entirely automatically a morphometric anatomical atlas from 3D medical image s acquired by CT-Scan or MR . We detail each step of the method, including the non-rigid registration algorithm, 3D lines averaging , and statistical analysis processes . We apply the method to obtain a quantitative atlas of crest lines of the skull . Finally, we use the resulting atlas to study a craniofacia l disease : we show how we can obtain qualitative and quantitative results by contrasting a skull affected by a deformation of th e mandible with the atlas .Dans cet article, nous présentons une méthode pour construire de manière automatique des atlas anatomiques morphométriques à partir d'images médicales tridimensionnelles obtenues par scanographie ou imagerie par résonance magnétique. Nous en détaillons les différentes étapes, en particulier les algorithmes de mise en correspondance non-rigide, de moyenne et d'analyse statistique de lignes caractéristiques tridimensionnelles. Nous appliquons la méthode à la construction d'un atlas morphométrique des lignes de crête du crâne. Nous montrons alors comment la comparaison automatique entre l'atlas et un crâne présentant une déformation mandibulaire permet d'obtenir des résultats qualitatifs et quantitatifs utilisables par un médecin

Statistical Computing on Non-Linear Spaces for Computational Anatomy

International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings

1 JPEG2000-BASED DATA HIDING TO SYNCHRONOUSLY UNIFY DISPARATE FACIAL DATA FOR SCALABLE 3D

www.lsis.org We present a scalable encoding strategy for the 3D facial data in various bandwidth scenarios. The scalability, needed to cater diverse clients, is achieved through the multiresolution characteristic of JPEG2000. The disparate 3D facial data is synchronously unified by the application of data hiding wherein the 2.5D facial model is embedded in the corresponding 2D texture in the discrete wavelet transform (DWT) domain. The unified file conforms to the JPEG2000 standard and thus no novel format is introduced. The method is effective and has the potential to be applied in videosurveillance and videoconference applications

6èmes Journées de Travail du GT Réalité Virtuelle

National audienc

Optimizing color information processing inside an SVM network

International audienceToday, with the higher computing power of CPUs and GPUs, many different neural network architectures have been proposed for object detection in images. However, these networks are often not optimized to process color information. In this paper, we propose a new method based on an SVM network, that efficiently extracts this color information. We describe different network archi-tectures and compare them with several color models (CIELAB, HSV, RGB...). The results obtained on real data show that our network is more efficient and robust than a single SVM network, with an average precision gain ranging from 1.5% to 6% with respect to the complexity of the test image database. We have optimized the network architecture in order to gain information from color data, thus increasing the average precision by up to 10%