Akademik Veri Yönetim Sistemi
33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Türkiye, 25 - 28 Haziran 2025, (Tam Metin Bildiri)
Dental radiography images are widely used medical images in dental diagnosis and analysis. However, they can suffer from quality degradation due to noise, low contrast and artifacts, which can negatively affect the accuracy of automated analysis methods. Preprocessing methods play an important role in manual and deep learning-based analysis by improving the quality of these images. In this study, various preprocessing techniques were applied using the Tufts Dental Database and the results were compared. The effects of noise reduction, contrast enhancement and sharpening methods are evaluated with MAE, MSE, PSNR and SSIM metrics. Experimental results show that certain preprocessing methods are effective in improving dental radiography image quality.