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An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images.

Leonardi R, Giordano D, Maiorana F - J. Biomed. Biotechnol. (2009)

Bottom Line: Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance.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.

View Article: PubMed Central - PubMed

Affiliation: Istituto di II Clinica Odontoiatrica, Policlinico Città Universitaria, Catania, Italy. rleonard@unict.it

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.

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Second feedback template for the Basion and CNN output.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig12: Second feedback template for the Basion and CNN output.

Mentions: This first CNN is able to highlight the Basion. The value of the template could be increased until at least a point in the ROI is highlighted. The CNN output is stored on the initial state of the CNN, and the template of Figure 12 is applied in order to follow the occipital bone (that must be present and have a sufficient length) and check the Basion against anatomical constraints. The template is applied with bias = 0.8 and 5 cycles.


An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images.

Leonardi R, Giordano D, Maiorana F - J. Biomed. Biotechnol. (2009)

Second feedback template for the Basion and CNN output.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2742650&req=5

fig12: Second feedback template for the Basion and CNN output.
Mentions: This first CNN is able to highlight the Basion. The value of the template could be increased until at least a point in the ROI is highlighted. The CNN output is stored on the initial state of the CNN, and the template of Figure 12 is applied in order to follow the occipital bone (that must be present and have a sufficient length) and check the Basion against anatomical constraints. The template is applied with bias = 0.8 and 5 cycles.

Bottom Line: Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance.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.

View Article: PubMed Central - PubMed

Affiliation: Istituto di II Clinica Odontoiatrica, Policlinico Città Universitaria, Catania, Italy. rleonard@unict.it

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.

Show MeSH