Segmentation of Anterior Tissues in Craniofacial Cone-Beam CT Images

Cone-beam computed tomography (CBCT) images are used in craniofacial research for diagnosing dentofacial deformities, skeletal malocclusion severity and to assist in virtual surgical planning. There is a need for automated guidance in predicting regions that could most benefit from surgical intervention. As a part of the effort to conduct such experiments, it is preferable to remove soft tissues in the craniofacial region in CBCT images. However, this front end "data preparation" step is non-trivial for CBCT images due to the inherent fluctuations in the intensity of tissues and bones caused by photon scattering of cone beam shaped X-rays during image acquisition. In this paper, we describe our automated segmentation approach for segmenting anterior tissues in more than 600 3D CBCT images with good result, by combining a selected set of 2D image processing techniques in conjunction with certain facial biometric parameters.