CLINIFACE: PHENOTYPIC VISUALISATION AND ANALYSIS USING NON-RIGID REGISTRATION OF 3D FACIAL IMAGES
- 1School of Earth and Planetary Sciences, Faculty of Science and Engineering, Curtin University, Perth, WA 6845, Australia
- 2Western Australian Register of Developmental Anomalies and Genetic Services of Western Australia, King Edward Memorial Hospital, Dept. of Health, Gov. of Western Australia, Perth WA 6008, Australia
- 3Telethon Kids Institute and Division of Paediatrics, Faculty of Health and Medical Sciences, UWA, Perth, WA 6008, Australia
Keywords: 3D Image, Anthropometrics, Cliniface, Dysmorphology, Facial Landmarks, Non-Rigid Registration, Phenotype
Abstract. Facial appearance has long been understood to offer insight into a person’s health. To an experienced clinician, atypical facial features may signify the presence of an underlying rare or genetic disease. Clinicians use their knowledge of how disease affects facial appearance along with the patient’s physiological and behavioural traits, and their medical history, to determine a diagnosis. Specialist expertise and experience is needed to make a dysmorphological facial analysis. Key to this is accurately assessing how a face is significantly different in shape and/or growth compared to expected norms. Modern photogrammetric systems can acquire detailed 3D images of the face which can be used to conduct a facial analysis in software with greater precision than can be obtained in person. Measurements from 3D facial images are already used as an alternative to direct measurement using instruments such as tape measures, rulers, or callipers. However, the ability to take accurate measurements – whether virtual or not – presupposes the assessor’s facility to accurately place the endpoints of the measuring tool at the positions of standardised anatomical facial landmarks. In this paper, we formally introduce Cliniface – a free and open source application that uses a recently published highly precise method of detecting facial landmarks from 3D facial images by non-rigidly transforming an anthropometric mask (AM) to the target face. Inter-landmark measurements are then used to automatically identify facial traits that may be of clinical significance. Herein, we show how non-experts with minimal guidance can use Cliniface to extract facial anthropometrics from a 3D facial image at a level of accuracy comparable to an expert. We further show that Cliniface itself is able to extract the same measurements at a similar level of accuracy – completely automatically.