title: Automatic landmarking of cephalograms using active appearance models.
abstract: There have been many attempts to further improve and automate cephalometric analysis in order to increase accuracy, reduce errors due to subjectivity, and to provide more efficient use of clinicians' time. The aim of this research was to evaluate an automated system for landmarking of cephalograms based on the use of an active appearance model (AAM) that contains a statistical model of shape and grey-level appearance of an object of interest and represents both shape and texture variations of the region covered by the model. Multi-resolution implementation was used, in which the AAM iterate to convergence at each level before projecting the current solution to the next level of the model. The AAM system was trained using 60 randomly selected, hand-annotated digital cephalograms of subjects between 7.2 and 25.6 years of age, and tested with a leave-five-out method that enabled testing not only of the accuracy of the AAM system but also the accuracy of each AAM. Differences between methods were examined using the non-parametric Wilcoxon signed rank test. An average accuracy of 1.68 mm was obtained, with 61 per cent of landmarks detected within 2 mm and 95 per cent of landmarks detected within 5 mm precision. A noticeable increase in overall precision and detection of low-contrast cephalometric landmarks was achieved compared with other automated systems. These results suggest that the AAM approach can adequately represent the average shape and texture variations of craniofacial structures on digital radiographs. As such it can successfully be implemented for automatic localization of cephalometric landmarks.
authors.affiliation: Department of Orthodontics, University of Novi Sad, Novi Sad, Serbia. pvucinic@neobee.net
authors.forename: Predrag
authors.initials: P
authors.lastname: Vucinić
authors.forename: Zeljen
authors.initials: Z
authors.lastname: Trpovski
authors.forename: Ivana
authors.initials: I
authors.lastname: Sćepan
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002508:Cephalometry / Q000379:methods* / Q000706:statistics & numerical data; D002648:Child; D005145:Face / Q000033:anatomy & histology; D005147:Facial Bones / Q000033:anatomy & histology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data; D008297:Male; D008311:Malocclusion, Angle Class I / Q000000981:diagnostic imaging; D008312:Malocclusion, Angle Class II / Q000000981:diagnostic imaging; D008313:Malocclusion, Angle Class III / Q000000981:diagnostic imaging; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D019252:Radiography, Dental, Digital / Q000379:methods / Q000706:statistics & numerical data; D055815:Young Adult