Caractérisation morphologique et mécanique de milieux poreux 3D (application à la microarchitecture osseuse)

De la géologie à l'étude de la matière, en passant par le génie civil, les milieux poreux balaient un large éventail de champs d'application et d'échelles. Les outils génériques de traitement de l'image 3D discrète développés dans cette thèse contribuent à la caractérisation de leur morphologie complexe et de leur comportement physique. Dans un premier temps, les travaux ont permis de proposer le squelette hybride, technique d'amincissement prenant en compte les formes locales de l'objet, et générant un squelette composé de surfaces 2D et de chemins 1D organisés dans l'espace. Ensuite, nous avons proposé et validé le protocole Hybrid Skeleton Graph Analysis (HSGA), afin de décomposer la structure d'un milieu poreux et d'extraire localement des paramètres morphologiques et topologiques caractéristiques. Une passerelle a été établie vers la modélisation par Eléments Finis, à l'aide de chaînes d'éléments poutres et de coques déterminées par une technique originale de triangulation de surfaces 2D discrètes appelée Surface Marching Cubes. Les modèles géométriques simplifiés obtenus permettent une estimation précise par simulation des propriétés physiques du matériau telles que sa rigidité ou sa densité, tout en étant peu coûteux en ressources. Enfin, deux études menées sur des images d'échantillons osseux ont permis de valider ces outils ainsi que d'évaluer leur potentiel dans le cadre de la problématique médicale du dépistage de l'ostéoporose.ORLEANS-BU Sciences (452342104) / SudocSudocFranceF

3D Image Analysis and Artificial Intelligence for Bone Disease Classification

International audienceIn order to prevent bone fractures due to disease and ageing of the population, and to detect problems while still in their early stages, 3D bone micro architecture needs to be investigated and characterized. Here, we have developed various image processing and simulation techniques to investigate bone micro architecture and its mechanical stiffness. We have evaluated morphological, topological and mechanical bone features using artificial intelligence methods. A clinical study is carried out on two populations of arthritic and osteoporotic bone samples. The performances of Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machines (SVM) and Genetic Algorithm (GA) in classifying the different samples have been compared. Results show that the best separation success (100 %) is achieved with Genetic Algorithm

A New Method for 3D Thinning of Hybrid Shaped Porous Media Using Artificial Intelligence. Application to Trabecular Bone

Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper presents an alternative to compute a hybrid shape-dependant skeleton and its application to porous media. The resulting skeleton combines 2D surfaces and 1D curves to represent respectively the plate-shaped and rod-shaped parts of the object. For this purpose, a new technique based on neural networks is proposed: cascade combinations of complex wavelet transform (CWT) and complex-valued artificial neural network (CVANN). The ability of the skeleton to characterize hybrid shaped porous media is demonstrated on a trabecular bone sample. Results show that the proposed method achieves high accuracy rates about 99.78%-99.97%. Especially, CWT (2nd level)-CVANN structure converges to optimum results as high accuracy rate-minimum time consumption

COMPARISON OF DİSCRETE WAVELET TRANSFORM AND COMPLEX WAVELET TRANSFORM IN HYBRID SKELETONIZATION BASED ON CVANN

Curve and surface thinning are widely-used skeletonization techniques for modeling objects in 3 dimensions. In the case of disordered porous media analysis, however, neither is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. This paper concludes an application of discrete wavelet transform (WT) and complex wavelet transform (CWT) in image processing problem such as hybrid skeletonization of trabecular bone images. Hybrid skeleton combines 2D surfaces and 1D curve to represent respectively the plate-shaped and rod-shaped parts of the object. For hybrid skeletonization, two cascade structures are proposed. In these structures, features of images were extracted with discrete wavelet transform and complex wavelet transform. After that, obtained features were used as inputs of complex-valued artificial neural network (CVANN) which is multi-layered artificial neural networks with two dimensions (real and imaginary parts). Effects of the feature extraction methods are compared for ability of the hybrid skeletonization on a trabecular bone sample. Results show that the CWT succeeded to hybrid skeletonization with lower error rate than WT