Enhancing diagnostic information in abdominal computed tomography (CT) images through optimized image enhancement techniques.

In medical imaging, particularly in enhancing computed tomography (CT) scan images, improving image quality while preserving diagnostic content is critical for detecting different types of abnormalities, especially in cases such as tumors, inflammatory conditions, or vascular issues. This paper proposes a novel image enhancement pipeline that integrates several image enhancement techniques into a sequential workflow that is specifically designed for abdominal CT scan images. The proposed pipeline combines windowing, contrast-limited adaptive histogram equalization, denoising via non-local means, and unsharp masking to concurrently address several issues affecting the quality of the images. Unlike existing methods, the proposed combinational approach improves contrast, suppresses noise, and sharpens structural detail, guaranteeing the balance between the enhancement and the diagnostic integrity. The workflow was evaluated on datasets from The Cancer Imaging Archive and the Medical Segmentation Decathlon. The proposed approach is assessed using key image quality metrics, yielding an average Peak Signal-to-Noise Ratio of 31.79 dB, Universal Image Quality Index of 0.96, Feature Similarity Index of 0.93, Absolute Mean Brightness Error of 7.12, and Edge Content of 7.78. These results indicate significant improvements in contrast enhancement, noise reduction, and the preservation of structural details. We performed an additional qualitative analysis by generating histograms and saliency maps that further confirm the method's effectiveness in enhancing the diagnostic quality of the CT images for both clinical and research purposes.