<![CDATA[Radiography is a medical imaging technique that utilizes X-ray radiation to obtain images of organs in the body, including the abdomen. Image quality is very important in supporting the accuracy of diagnosis and can be measured objectively through the Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) parameters. As digital technology advances, Python-based image processing offers significant potential in improving the visual and diagnostic quality of radiographic images. This study aims to analyze the effectiveness of digital image processing techniques in improving the quality of computed radiography (CR) radiography, especially in terms of increasing SNR and CNR values. This study uses an experimental approach with CR radiographic image data obtained from dr. Gunawan Mangunkusumo Ambarawa Hospital. The image was processed using the Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms in the Python platform. The results of the analysis showed that both methods were able to increase the SNR and CNR values, with the Equalization Histogram resulting in the highest CNR of 24.09, while the CLAHE achieved a maximum value of 16.34. Although Histogram Equalization improves global contrast, this method tends to reduce local details. In contrast, CLAHE shows excellence in maintaining anatomical structure and providing a more even contrast increase. Thus, Python-based digital image processing has proven to be effective in improving the quality of abdominal radiographic images and has the potential to be a reliable diagnostic tool in modern radiology practice.]]>
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