Forensic facial reconstruction may be the final option available to draw the public attention in cases where the identity of an individual cannot be established by standard identification methods. Two fundamental components of all forensic facial reconstruction techniques are cranial morphology and soft tissue depths databases. The purpose of this study was to extend such databases by providing a complete set of accurate facial soft tissue thickness measurements, acquired from a contemporary adult population from Germany, for use in forensic facial reconstruction. The aims were to measure the distance between well-defined landmarks on the skull and reference points on the face in a standardised manner, to analyse how sex, age and body mass index (BMI) influence facial soft tissue depths, to identify patterns of facial asymmetry, and to conduct a comparative analysis with other populations. The material for this study consisted of 320 (160 male, 160 female) anonymised multi-slice computerised tomography (MSCT) scans of individuals drawn from a German population. Individuals between the ages of 18 and 84 years were analysed. Their statures varied between 1.50 m and 1.96 m; their weights ranged between 40 kg and 145 kg. The BMI fluctuated between 16.6 kg/m2 and 45.8 kg/m2. Patients with severe trauma or pathologies that may compromise facial soft tissue depth were excluded from the study as were patients known to have been treated with specific medication (e.g. cortisone). In Amira®, 3D models of the surfaces of the skull and the facial skin were semi-automatically segmented using previously calculated thresholds and surface extraction algorithms. The parameters were adjusted to permit semi-transparent visualisation and examination of the structures of both the 3D skull and facial skin surface models simultaneously. Facial soft tissue depth was measured at 10 midline and 28 bilateral anatomical landmarks, according to the main orientations of the skull. Statistical analyses and tests were performed with SPSS® Version 22 and TDStats Version v2015.1. The analysis of facial soft tissue thickness versus BMI, sex and age, for each landmark separately, indicated that, at a number of the landmarks, facial soft tissue depth is significantly (p < 0.05) influenced by all three biometric variables. Facial soft tissue thickness increased with increasing BMI, but the correlations with age were insignificant. The differences between males and females were statistically significant (p < 0.05) for almost all anatomical landmarks with the exception of a few in the region of the nasal root and orbitals. Asymmetry was noted at over half of the bilateral landmarks. The differences between the results from this sample and those obtained from comparable databases contradict the hypothesis that population-specificity significantly influences facial soft tissue thickness. Nevertheless, this and future studies of craniofacial soft tissues will improve our knowledge of the complexity of the human face. The information gathered will be invaluable when considering forensic facial reconstruction methods for neighbouring populations.

Introduction: Zygomatic fractures form a major entity in craniomaxillofacial traumatology. Few studies have dealt with biomechanical basics and none with the role of the facial soft tissues. Therefore this study should investigate, whether facial soft tissue plays a protecting role in lateral midfacial trauma.

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.

Introduction: Zygomatic fractures form a major entity in craniomaxillofacial traumatology. Few studies have dealt with biomechanical basics and none with the role of the facial soft tissues. Therefore this study should investigate, whether facial soft tissue plays a protecting role in lateral midfacial trauma.