Cephalometric landmark detection: Artificial intelligence vs human examination

Magister Scientiae Dentium - MSc(Dent)Cephalometric landmark detection is important for accurate diagnosis and treatment planning. The most common cause of random errors, in both computer-aided cephalometry and manual cephalometric analysis, is inconsistency in landmark detection. These methods are time-consuming. As a result, attempts have been made to automate cephalometric analysis, to improve the accuracy and precision of landmark detection whilst also minimizing errors caused by clinician subjectivity.This mini-thesis aimed to determine the precision of two cephalometric landmark identification methods, namely an artificial intelligence programme (BoneFinder®) and a computer-assisted examination software (Dolphin ImagingTM)

Heritability of facial morphology

Facial recognition methodologies, widely used today in everything from automatic passport controls at airports to unlocking devices on mobile phones, has developed greatly in recent years. The methodologies vary from feature based landmark comparisons in 2D and 3D, utilising Principal Component Analysis (PCA) to surface-based Iterative Closest Point Algorithm (ICP) analysis and a wide variety of techniques in between. The aim of all facial recognition software (FCS) is to find or match a target face with a reference face of a known individual from an existing database. FCS, however, faces many challenges including temporal variations due to development/ageing and variations in facial expression. To determine any quantifiable heritability of facial morphology using this resource, one has to look for faces with enough demonstrable similarities to predict a possible genetic link, instead of the ordinary matching of the same individual’s face in different instances. With the exception of identical twins, this means the introduction of many more variables into the equation of how to relate faces to each other. Variation due to both developmental and degenerative aging becomes a much greater issue than in previous matching situations, especially when comparing parents with children. Additionally, sexual dimorphism is encountered with cross gender relationships, for example, between mothers and sons. Non-inherited variables are also encountered such as BMI, facial disfigurement and the effects of dental work and tooth loss. For this study a Trimmed Iterative Closest Point Algorithm (TrICP) was applied to three-dimensional surfaces scans, created using a white light scanner and Flexscan 3D, of the faces of 41 families consisting of 139 individuals. The TrICP algorithm produced 7176 Mesh-to-mesh Values (MMV) for each of seven sections of the face (Whole face, Eyes, Nose, Mouth, Eyes-Nose, Eyes-Nose-Mouth, and Eyes-Nose- Mouth-Chin). Receiver Operated Characteristic (ROC) analysis was then conducted for each of the seven sections of the face within 11 predetermined categories of relationship, in order to assess the utility of the method for predicting familial relationships (sensitivity/specificity). Additionally, the MMVs of three single features, (eyes, nose and mouth) were combined to form four combination areas which were analysed within the same 11 relationship categories. Overall the relationship between sisters showed the most similarity across all areas of the face with the clear exception of the mouth. Where female to female comparison was conducted the mouth consistently negatively affected the results. The father-daughter relationship showed the least similarity overall and was only significant for three of the 11 portions of the face. In general, the combination of three single features achieved greater accuracy as shown by Areas Under the Curve (AUC) than all other portions of the face and single features were less predictive than the face as a whole

Computer aided detection of natural reference markers in serial radiographs of growing bone

This thesis describes the theoretical and experimental development and testing of a computer-based image processing system for the detection of stable structural features in serial radiographs of growing bone. The study is divided into three parts. First, a simple theoretical model for the detection of stable structures in a radiographic sequence was developed together with a more detailed statistical model of the processes which hinder detection. Analysis of the models indicated that the processing procedures needed to perform the detection were those described by the so-called "matched filter equation". Secondly, the assumptions of the model were tested in a series of experiments using serial radiographs of 24 children with implanted tantalum markers, drawn from the files of the University of Washington, USA. Information gained from these experiments prompted changes to the detection method to allow the radiographic data to comply with these assumptions and provide robustness against image noise. The method was implemented as a semi-automatic image processing system on a Sun 3/160 computer. The system was tested in a further series of experiments using a second sample of radiographs of 28 children with implanted markers and with radiographs of dried human skulls. The results of these experiments indicated that the system could detect persistent structural features whose positions were consistent with the stable tantalum markers and that the system was robust against minor changes in projection between radiographs. Finally, the detection system was applied to the investigation of mandibular incisor eruption from intra-alveolar to pubertal eruption stages using serial lateral cephalometric radiographs of 11 children. This investigation revealed two new features of incisor eruption: first, the eruption paths deviated lingually at, or just prior to, alveolar emergence; and second, post-emergence eruption was characterised by a labio-lingual oscillation of the crowns of the incisors