Segmentation of individual facial soft tissues has received relatively little attention in the literature due to the complicated structures of these tissues. There is a need to incorporate the prior information, which is usually in the form of atlases, in the segmentation process. In this thesis we performed several segmentation methods that take advantage of prior knowledge for facial soft tissue segmentation. An atlas based method and three expectation maximization &ndash / Markov random field (EM-MRF) based methods are tested for two dimensional (2D) segmentation of masseter muscle in the face. Atlas based method uses the manually labeled atlases as prior information. We implemented EM-MRF based method in different manners / without prior information, with prior information for initialization and with using labeled atlas as prior information. The differences between these methods and the influence of the prior information are discussed by comparing the results. Finally a new method based on EM-MRF is proposed in this study. In this method we aim to use prior information without performing manual segmentation, which is a very complicated and time consuming task. 10 MRI sets are used as experimental data in this study and leave-one-out technique is used to perform segmentation for all sets. The test data is modeled as a Markov Random Field where unlabeled training data, i.e., other 9 sets, are used as prior information. The model parameters are estimated by the Maximum Likelihood approach when the Expectation Maximization iterations are used to handle hidden labels. The performance of all segmentation methods are computed and compared to the manual segmented ground truth. Then we used the new 2D segmentation method for three dimensional (3D) segmentation of two masseter and two temporalis tissues in each data set and visualize the segmented tissue volumes.

Cette thèse avait deux buts principaux : a) l'implémentation et l'amélioration d'une méthode de calcul de densité volumique du sein (VBD), et b) la proposition d'une mesure d'irradiation utilisable pour l'évaluation du risque individuel en mammographie avec une méthode pour l'estimer. La densité du sein est connue comme indicateur de risque du cancer. Une méthode de quantification objective de la VBD a été développée, à partir d'approches existantes, et améliorée. La méthode a été implémentée pour deux systèmes de mammographie. Elle repose sur l'étalonnage du système de mammographie et la chaîne d'acquisition avec des fantômes équivalents aux tissus mammaires. Une carte de densité est calculée.La contribution majeure de la thèse consiste en une nouvelle méthode de validation, applicable à tout calcul de VBD d'image de mammographie. Elle consiste à comparer les résultats aux valeurs de densité obtenues par des scanners thoraciques pour la même patiente. Cette validation a été appliquée à notre méthode de calcul et nous avons trouvé 10% d'écart moyen entre les deux méthodes, ce qui est comparable aux résultats de l'état de l'art. Pour le risque d'irradiation individuel, nous proposons de remplacer la dose glandulaire moyenne par l'énergie déposée, qui dépend de la quantité et de la distribution du tissu glandulaire, qui est le tissu à risque. L'énergie volumique déposée est calculée par simulation de Monte Carlo. Le VBD, calculé pour l'image de projection à 0° en tomosynthèse, aide à localiser le tissu glandulaire et à attribuer l'énergie déposée dans les tissus différents. Une proposition a été faite pour des fantômes géométriques, un fantôme texturé et un cas de patiente / In this research project the main goals were a) to implement a method for the computation of the volumetric breast density (VBD), and b) to propose an improved quantity for the assessment of individual radiation-induced risk, in particular during mammography, together with a method to quantify it. The breast density is known as a breast cancer risk factor. The objective quantification of the volumetric breast density was developed, based on already published methods, and improved. The method was implemented for two mammography systems. It is based on the calibration of the mammography system acquisition chain with breast equivalent phantoms and computes a breast density map. Our most important contribution resides in a new validation method applicable to any VBD computation, consisting in comparing its results with the VBD obtained from a thorax CT examination for the same patient. This validation method was applied to our VBD computation. We found an average deviation between mammography and CT of less than 10%. Our results are comparable to the state-of-the-art results for other validation methods. For the individual radiation risk, we proposed to replace the average glandular dose by the imparted energy, which depends on the quantity and distribution of the glandular tissue, which is the tissue at risk. The volumetric imparted energy is computed from Monte Carlo simulations. The VBD, computed for the 0° projection of tomosynthesis exams, helps us to localize the glandular tissue and to attribute the imparted energy to the different tissues. A proposition was implemented for geometric phantoms, a textured phantom and a patient case.