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Journal of Biomedicine and Biotechnology
Volume 2009 (2009), Article ID 717102, 12 pages
http://dx.doi.org/10.1155/2009/717102
Research Article

An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images

1Istituto di II Clinica Odontoiatrica, Policlinico Città Universitaria, Via S. Sofia 78, 95123 Catania, Italy
2Dipartimento di Ingegneria Informatica e Telecomunicazioni, Università di Catania, Viale A. Doria 6, 95125 Catania, Italy

Received 16 February 2009; Revised 16 May 2009; Accepted 18 June 2009

Academic Editor: Rita Casadio

Copyright © 2009 Rosalia Leonardi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged.