Stereo-assisted landmark detection for the analysis of changes in 3-D facial shape

In this paper, a semi-automated approach to 3-D landmark digitization of the face is described which uses a combination of active shape model-driven feature detection and stereophotogrammetric analysis. The study aims to assess whether the proposed method is capable of detecting statistically significant changes in facial soft tissue shape due to mandibular repositioning in a cross-sectional patient sample. A hybrid stereophotogrammetric and structured-light imaging system is used for acquiring 3-D face models in the first instance. A landmark-based statistical analysis of facial shape change is then carried out using procrustes registration, principal component analysis and thin plate spline warping on the 2-D facial midline profiles and automatically digitized 3-D landmarks. The proposed method is validated both statistically and visually by characterizing shape changes induced by mandibular repositioning in a heterogeneous cross-sample of 20 orthodontic patients. It is shown that the method is capable of distinguishing between changes in facial morphology due to simulated surgical correction and changes due to other factors such as growth and normal variation within the patient sample. The study shows that the proposed method may be useful for auditing outcomes of clinical treatment or surgical intervention which result in changes to facial soft tissue morphology.