Comparative Assessment of Various Image Processing Algorithms in Identifying the Location of Endodontic Files
Objectives: To investigate the effect of image processing algorithms in identifying the locations of endodontic files when using direct sensor digital intraoral radiography.
Materials and Methods: Thirty extracted permanent singlerooted human teeth with single root canals were prepared with standard access cavities. Endodontic files (sizes 08, 10 and 15 k-file in sequence) were positioned first at the apex and radiographed and then 1.0 mm short of the apex and radiographed. Standardized images were obtained using the RVG 5000, RVG 6100 and Sopix sensor as receptors. The original images were then processed with four processing algorithms, invert, smooth, contrast enhancement and median filter, respectively. Six observers evaluated all the images independently and repeated with 10% of all images two weeks afterward. Receiver operating characteristic (ROC) analyses were performed and the areas under the curves were calculated. Two-way ANOVA and Bonferroni test (p=0.05) were performed. Kappa analysis was used to test the observer agreements.
Results: The mean Az for the invert and contrast enhancement images were higher than those of the originals. The mean Az for the median filter were lower than those of the originals. There was a significant difference between the mean Az of the invert and the median filter (p=0.002). There was a significant difference among the 3 types of sensors. (p< 0.001) The RVG 6100 sensor had the hightest mean Az, followed by the RVG 5000 and Sopix. There was a significant difference among endodontic file size (p<0.001). The Az increased with increasing file size. The Kappa index for inter-observer agreement ranged from 0.265 to 1.00.
Conclusions: The invert and contrast enhancement algorithms may help improve the ability to identify the location of endodontic files, whereas the median filter is not recommended. There was a significant difference among the three sensors. The sensor with higher resolution gave higher accuracy in locating file tips. In addition, the file size also played an important role in identifying the location of the file. The bigger the file size, the higher the detection accuracy.
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