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Digital dental radiographs : influence of image quality and software manipulation on their interpretation Delantoni, Antigoni

Abstract

With the technologic advances in digital systems there has been a development in the field of digital imaging. Systems have developed that allow us to take radiographs with a reduced patient exposure and higher convenience compared to conventional film. In many cases the information is electronically transferred either to get a second opinion or to share information. We need to know to what extent there is significant information loss with digital systems. It is therefore useful to compare the image quality of digital systems to conventional film. The aim of this study was to evaluate the amount of diagnostic information loss during the scanning of conventional radiographs by comparing images of different qualities for periodontal defects. Radiographs were taken and scanned at different resolutions (600,300,150 and 75dpi). A visual analog scale was used for the subjective assessment of image quality. Observers were asked to mark points on the selected images. Analysis of Variance was used to demonstrate significant differences. From the results we obtained from our data, no significant differences were found between the resolutions of 600 and 300 dpi, while there were differences between low quality images (75 dpi) and the higher ones. We drew conclusions as to whether there is significant amount of information loss during the scanning of radiographs in cases of periodontal defects. In addition to the periodontal model, further study on image manipulation by blurring was done using an endodontic model, and the subjective and objective performances and measurements of the observers were compared. The feature of blurring was selected since it is a feature present in Microsoft PowerPoint and no studies had been done on it in the past. The images were blurred using a blurring feature provided by software by the NIH image, and a comparison was made between blurred and non-blurred images for good and poor quality images. Differences were found in the points' distribution and the measurements between the two features. We concluded that blurring worsens good quality images, while poor quality images are improved.

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