Craniofacial Reconstruction Based on Skull-face Models Extracted from MRI Datasets

Abstract

We present a method for extracting skull and face models from MRI datasets and show how the resulting dataset is used in a craniofacial reconstruction (CFR) system. Datasets for 60 individuals are used to produce a database of 3D skull-face models, which are then used to give faces to unknown skulls. In addition to the skull-face geometry, other information about the individuals is known and can be used to aid the reconstruction process. The results of the system were evaluated using different criteria providing the system with different combinations of age, gender, body build and geometric skull features. Based on a surface to surface distance metric, the real and estimated faces produced were compared using different head models from the database with a leave-one-out strategy. The reconstruction scores obtained with our CFR system were comparable in magnitude (average distance less than 2.0 mm) to other craniofacial reconstruction systems. The results suggest that it is possible to obtain acceptable face estimations in a CFR system based on skull-face information derived from MRI data.