3D segmentation of maxilla in cone-beam computed tomography
imaging using base invariant wavelet active shape model on customized
two-manifold topology
Affiliations: Department of Electrical and Computer Engineering,
Texas A&M University, College Station, TX, USA | Department of Oral and Maxillofacial Surgery, The
Methodist Hospital Research Institute, Houston, TX, USA | Department of Mechanical Engineering, National Cheng
Kung University, Tainan, Taiwan | Department of Surgery (Oral and Maxillofacial
Surgery), Weil Medical College of Cornell University, Houston, TX, USA | Center for Biotechnology and Informatics, The
Methodist Hospital Research Institute, Houston, TX, USA | Departments of Pediatric Surgery and Orthodontics,
University of Texas Health Science Center, Houston, TX, USA | Department of Radiology, The Methodist Hospital, Weill
Medical College of Cornell University, Houston, TX, USA
Note: [] Corresponding author: Xiaobo Zhou, Center for Biotechnology and
Informatics, The Methodist Hospital Research Institute and Department of
Radiology, The Methodist Hospital, Weill Medical College of Cornell University,
Houston, TX 77030, USA. E-mail: XZhou@tmhs.org
Abstract: Recent advances in cone-beam computed tomography (CBCT) have rapidly
enabled widepsread applications of dentomaxillofacial imaging and orthodontic
practices in the past decades due to its low radiation dose, high spatial
resolution, and accessibility. However, low contrast resolution in CBCT image
has become its major limitation in building skull models. Intensive
hand-segmentation is usually required to reconstruct the skull models. One of
the regions affected by this limitation the most is the thin bone images. This
paper presents a novel segmentation approach based on wavelet density model
(WDM) for a particular interest in the outer surface of anterior wall of
maxilla. Nineteen CBCT datasets are used to conduct two experiments. This
mode-based segmentation approach is validated and compared with three different
segmentation approaches. The results show that the performance of this
model-based segmentation approach is better than those of the other approaches.
It can achieve 0.25 ± 0.2 mm of surface error from ground truth of bone surface.
Keywords: 3D segmentation, active shape model (ASM), statistical shape model (SSM), craniomaxillofacial (CMF) surgeries, cone-beam computed tomography (CBCT)
