<![CDATA[Background: Non-contrast abdominal radiography is an important diagnostic procedure in radiology for detecting abnormalities in the abdominal organs. Image quality is evaluated through the Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) to measure the signal-tonoise ratio and the ability to distinguish contrast between anatomical structures. Image processing using Python allows for improved image quality at low exposures, in line with the ALARA (As Low As Reasonably Achievable) principle of minimizing radiation dose without compromising diagnostic accuracy. However, studies on optimizing non-contrast abdominal images using this technique are still limited, particularly in reducing the effects of scattered radiation on thick objects such as the abdomen. Methods: This study employed a quantitative experimental approach. An adult abdominal phantom was used. The study was conducted in the Radiology Laboratory of Universitas ‘Aisyiyah Yogyakarta, from March 2025 to May 2025. Data collection was conducted through documentation and processing using Python on Google Colab. The SNR and CNR values of noncontrast abdominal images before and after image enhancement were calculated using Non-Local Means (NLM) and Histogram Equalization (HE). The Shapiro-Wilk normality test and paired sample t-test were then performed. Results: The calculated SNR value increased from an average of 8.73 to 11.66, and the CNR increased from an average of 1.69 to 4.28. The data were normally distributed (p>0.05), and there was a significant difference in SNR (p=0.0019) and CNR (p=0.0003) before and after enhancement (p<0.05). Conclusion: Based on the results of this study, image processing using Python effectively improves the quality of non-contrast abdominal radiographic images at low exposures, supporting diagnostic accuracy and reducing radiation dose.]]>
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