Enhancing the Quality of Digital Panoramic Radiographs with Median Filtering and Histogram Equalization Techniques

Moh. Yusuf; Shella Indri Novianty; Bella Maharani

doi:10.37034/medinftech.v3i4.114

Authors

Moh. Yusuf Sultan Agung Islamic University
Shella Indri Novianty Sultan Agung Islamic University
Bella Maharani Sultan Agung Islamic University

DOI:

https://doi.org/10.37034/medinftech.v3i4.114

Keywords:

Contrast Enhancement, Digital Panoramic Radiography, Image Enhancement, Medical Image Processing, Noise Reduction

Abstract

Digital panoramic radiographs often suffer from noise and reduced contrast, which can compromise diagnostic accuracy and treatment planning. Conventional enhancement methods rely on default Carestream software with manual adjustments, which may be inconsistent and time-consuming. This study aims to improve the quality of digital panoramic radiographs by applying Median Filtering (MF) to reduce noise and Histogram Equalization (HE) to enhance contrast using MATLAB R2022a. An analytical experimental design was conducted on 155 digital panoramic radiograph images, sampled from a total population of 254 images collected between July 2021 and July 2022 at the Dental Radiology Installation, Islamic Dental and Oral Education Hospital of Sultan Agung Semarang (RSIGMP-SA), using the Slovin formula. Radiograph files in DICOM format were converted to JPEG for analysis. Image quality was evaluated using Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), and statistical significance was analyzed with the paired T-test in SPSS. The results indicated normal distribution for all parameters (p>0.05) and significant improvements in SNR and CNR after enhancement (T=-14.426 for SNR, T=-41.673 for CNR, p<0.05). These findings demonstrate that MF effectively enhances image sharpness, while HE improves contrast, resulting in clearer and more diagnostically reliable panoramic radiographs. The proposed approach provides a standardized and reproducible method for image quality improvement and offers potential support for clinical decision-making.

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