Objective: To describe the techniques used for automatic landmarking of cephalograms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in locating each cephalometric point. Materials and Methods: The literature survey was performed by searching the Medline, the Institute of Electrical and Electronics Engineers, and the ISI Web of Science Citation Index databases. The survey covered the period from January 1966 to August 2006. Abstracts that appeared to fulfill the initial selection criteria were selected by consensus. The original articles were then retrieved. Their references were also hand-searched for possible missing articles. The search strategy resulted in 118 articles of which eight met the inclusion criteria. Many articles were rejected for different reasons; among these, the most frequent was that results of accuracy for automatic landmark recognition were presented as a percentage of success. Results: A marked difference in results was found between the included studies consisting of heterogeneity in the performance of techniques to detect the same landmark. All in all, hybrid approaches detected cephalometric points with a higher accuracy in contrast to the results for the same points obtained by the model-based, image filtering plus knowledge-based landmark search and ''soft-computing'' approaches. Conclusions: The systems described in the literature are not accurate enough to allow their use for clinical purposes. Errors in landmark detection were greater than those expected with manual tracing and, therefore, the scientific evidence supporting the use of automatic landmarking is low.
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.
The aim of this study was to evaluate the accuracy of some commonly used cephalometric landmarks of monitor-displayed images with and without image emboss enhancement. The following null hypothesis was tested: there is no improvement in landmark detection accuracy between monitor-displayed images, with and without image embossing enhancement. Forty lateral cephalometric radiographs, taken from the data files of subjects were used in this study. A purpose-made software allowed recording of the cephalometric points and then, with the help of algorithms based on cellular neural networks, to transfer the previously processed radiographs into an embossed image. Five observers recorded 22 landmarks on the displayed images from the two image modalities, i.e. monitor-displayed radiograph (mode A) and monitor-displayed embossed radiograph (mode B). The positions of the landmarks were recorded and saved in the format of x and y co-ordinates and as Euclidean distance. The mean errors and standard deviation of landmarks location according to the two modalities were compared with the 'best estimate' for each landmark and the values were calculated for each of the 22 landmarks. One-way analysis of variance was then used to evaluate any statistically significant differences. Euclidean distance mean errors were higher for the embossed images (except for Po) than for the unfiltered radiographs. These differences were all statistically significant (P < 0.05) except for Or, Po, PM, Co, APOcc, and PPOcc. On the x and y co-ordinates, the accuracy of the cephalometric landmark detection improved on the embossed radiograph but only for a few points (Or on x axis and Po, PM, Co, and APOcc on y axis), as these were not statistically significant. The use of radiographic enhancement techniques, such as embossing, does not improve the level of accuracy for cephalometric points detection. Unless more precise algorithms are designed, this feature should not be used for clinical or research purposes.
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