title: Automatic computerized radiographic identification of cephalometric landmarks.
abstract: Computerized cephalometric analysis currently requires manual identification of landmark locations. This process is time-consuming and limited in accuracy. The purpose of this study was to develop and test a novel method for automatic computer identification of cephalometric landmarks. Spatial spectroscopy (SS) is a computerized method that identifies image structure on the basis of a convolution of the image with a set of filters followed by a decision method using statistical pattern recognition techniques. By this method, characteristic features are used to recognize anatomic structures. This study compared manual identification on a computer monitor and the SS automatic method for landmark identification on minimum resolution images (0.16 cm2 per pixel). Minimum resolution (defined as the lowest resolution at which a cephalometric structure could be identified) was used to reduce computational time and memory requirements during this development stage of the SS method. Fifteen landmarks were selected on a set of 14 test images. The results showed no statistical difference (p > 0.05) in mean landmark identification errors between manual identification on the computer display and automatic identification using SS. We conclude that SS shows potential for the automatic detection of landmarks, which is an important step in the development of a completely automatic cephalometric analysis.
authors.affiliation: Department of Orthodontics, University of California Los Angeles, USA.
authors.forename: D J
authors.initials: DJ
authors.lastname: Rudolph
authors.forename: P M
authors.initials: PM
authors.lastname: Sinclair
authors.forename: J M
authors.initials: JM
authors.lastname: Coggins
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002508:Cephalometry / Q000379:methods* / Q000706:statistics & numerical data; D002648:Child; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008297:Male; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted