Automatic classification of facial morphology for medical applications

Facial morphology measurement and classification play important role in the face anthropometry of many medical applications. This usually involves the investigation of medical abnormalities where specific facial features are studied by taking a number of measurements of the facial area under investigation. The measurements are often obtained from the three-dimensional (3D) scans of the faces; however, the measurements are often made manually, which is tedious and time consuming process. Moreover, in gene related studies thousands of measurements may be necessary in order to find statistically significant relationships between facial features and genes. Normative studies, from which typical populous models can be built, also require many measurements. Thus an automatic method to extract morphological measurements and interpret them is desirable. In this article, an automatic method for classification of facial morphology on the basis of a number of geometric measurements obtained automatically from 3D facial scans is presented. Among different facial features the philtrum, which is the vertical groove extending from the nose to the upper lip and the lip area, plays an important role in defining the interaction between the genes and craniofacial anomalies such as, for example, cleft lip and palate. In this paper, geometric features are analysed for their suitability to classify philtrum into three classes previously proposed by medical experts. Moreover, further analysis is conducted to assess the best number of classes to model the underlying data distribution from the point of view of classification accuracy. The obtained classification results are compared with the ground truth manual labelling of 3D face meshes provided by a medical expert. The dataset used for this research is taken from ALSPAC dataset and consists of 1000 3D face meshes. The proposed method achieves classification accuracy of 97% for this data set using the Mean, Minimum and Maximum curvature features in combination

Applying an automated method of classifying lip morphological traits

Objective: To apply an automated computerised method to categorise and determine the prevalence of different types of lip traits, and to explore associations between lip traits and sex differences. Design: Observational descriptive study utilising an automated method of facial assessment. Setting and participants: A total of 4747 children from the Avon Longitudinal Study of Parents and Children (ALSPAC) who each had 3D facial scans carried out at 15 years of age. Methods: Each of the participants was automatically categorised regarding predetermined lip morphological traits. Descriptive statistics were applied to report the prevalence of the different types of each trait, and chi-square tests were used to investigate sex differences and associations between traits. Results: A total of 4730 individuals were assessed (47% male, 53% female). Eight predetermined lip traits have been reported previously. There were differences in prevalence for all lip traits in male and female patients (all P ⩽ 0.0002), with differences between the sexes described for each trait. For example, a deeply grooved philtrum of average width was more prevalent in boys, and an indentation near the upper vermilion border was more prevalent in girls. Each of the traits was significantly associated with the other traits (all P < 0.0001), with particularly strong associations seen between traits in the same region (e.g. upper lip). Individual associations between traits are reported; for example, a straight lip contour was found to be associated with no true vermilion border in both the upper and lower lip regions. Conclusion: The automated computerised method described is an invaluable tool for the categorisation of lip morphological traits. The prevalence of various types of traits has been described. Sexual dimorphism exists for all the lip traits assessed. Generally, each of the traits are associated with all other traits, with individual associations reported

The effect of maternal smoking and alcohol consumption on lip morphology

Objective: To determine whether maternal smoking and/or alcohol consumption has an influence on lip morphology. Maternal smoking is a known risk factor for orofacial clefts; however, its influence on normal lip variation is unknown. Recent research regarding normal lip morphology has been contradictory. Design: Retrospective cohort study. Setting and participants: A total of 4747 children from the Avon Longitudinal Study of Parents and Children (ALSPAC) who each had 3D facial scans carried out at 15 years of age were included in the study. Methods: Each of the participants was automatically categorised regarding predetermined lip morphological traits. Questionnaires completed by their mothers identified smoking and alcohol habits during pregnancy. Logistic regression analyses were applied to determine the effect of maternal smoking and alcohol consumption on lip morphology. Results: Maternal smoking has significant effects on upper and lower lip contours, Cupid’s bow, lower lip-chin shape and lower lip tone (all P 6 units of alcohol are consumed per week (P = 0.0149, 32 weeks). Overall results suggest a deeply grooved philtrum is more likely if alcohol is consumed. Investigating the combined effect of smoking and alcohol consumption, lower lip contour (P = 0.00923) and lower lip-chin shape (P = 0.0171) are statistically significant, with lower lip contour more likely to be narrow in the midline, and lower lip-chin shape more likely to be an angular concavity. Conclusion: Maternal smoking influences a number of lip traits, including a possible epigenetic effect on upper lip contour. Maternal alcohol consumption, particularly at a high level, influences philtrum shape. Maternal smoking and alcohol consumption have a combined effect on lower lip contour and lower lip-chin shape

An automatic approach for classification and categorisation of lip morphological traits

Classification of facial traits (e.g., lip shape) is an important area of medical research, for example, in determining associations between lip traits and genetic variants which may lead to a cleft lip. In clinical situations, classification of facial traits is usually performed subjectively directly on the individual or recorded later from a three-dimensional image, which is time consuming and prone to operator errors. The present study proposes, for the first time, an automatic approach for the classification and categorisation of lip area traits. Our approach uses novel three-dimensional geometric features based on surface curvatures measured along geodesic paths between anthropometric landmarks. Different combinations of geodesic features are analysed and compared. The effect of automatically identified categories on the face is visualised using a partial least squares method. The method was applied to the classification and categorisation of six lip shape traits (philtrum, Cupid’s bow, lip contours, lip-chin, and lower lip tone) in a large sample of 4747 faces of normal British Western European descents. The proposed method demonstrates correct automatic classification rate of up to 90%