Introducing Radex: Adaptive Parameterized Feature Extraction from Medical Images

High-quality imaging is crucial in medical diagnostics, especially for detecting and assessing diseases through medical images. Current challenges in extracting subtle but critical features from these images are often due to the limitations of existing imaging transformation techniques. To address these challenges, this paper introduces a novel nonlinear transform, termed the Radex transform, which utilizes adaptive parameterization to enhance feature extraction. This innovative approach not only aims to improve the visualization of complex features within X-rays but also provides a dynamic method for adjusting transformation parameters to optimize image quality and diagnostic accuracy. We demonstrate that the Radex transform significantly outperforms traditional imaging and Radon transform techniques in terms of accuracy when applied to X-ray datasets. This new feature extraction technique is particularly advantageous for images with critical data along displayed lines and fissures, offering substantial improvements in the detection and analysis of pulmonary diseases.