Differences in Contrast Quality of Digital Panoramic Radiographs Before and After Contrast Stretching
DOI:
https://doi.org/10.37034/medinftech.v4i1.115Keywords:
Contrast Stretching, Contrast-to-Noise Ratio (CNR), Digital Panoramic Radiography, Thoracic image enhancement, Signal-to-Noise Ratio (SNR)Abstract
Digital panoramic radiography is widely used for diagnosis and treatment planning because it provides comprehensive information on dental and maxillofacial anatomical structures in digital form that can be directly visualized on a computer screen. However, the quality of panoramic radiographs may decrease due to noise, inadequate density, and low contrast, which can affect diagnostic interpretation. The contrast stretching method can be applied to address this problem by increasing image contrast and reducing noise, thereby improving the visibility of objects and anatomical boundaries in radiographic images. This study aimed to determine the effect of the contrast stretching method on the quality of digital panoramic radiographs. A quantitative experimental analysis was conducted using retrospective digital panoramic radiograph data from patients at RSIGMP UNISSULA. A total of 155 digital panoramic radiograph images from July 2021 to July 2022 were selected using the Slovin method. Image quality enhancement was quantitatively evaluated using the Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) parameters. The obtained data were analyzed using a paired t-test after fulfilling the normality assumption. The results showed significant differences in both SNR and CNR values before and after processing, with a significance value of 0.000 (p < 0.05). Both parameters increased after contrast stretching, indicating improved image contrast and reduced noise in digital panoramic radiographs. These findings demonstrate that contrast stretching is an effective and practical method for improving radiographic quality, which may support radiographers in achieving clearer diagnostic images and provide useful insight for medical imaging system developers in designing image enhancement modules.
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