X-ray Enhancement based on Component Attenuation, Contrast Adjustment and Image Fusion

Inspecting X-ray images is an essential aspect of medical diagnosis. However, due to an X-ray’s low contrast and low dynamic range, important aspects such as organs, bones, and nodules become difficult to identify. Hence, contrast adjustment is critical, especially in view of its ability to enhance the details in both bright and dark regions. 

we proposed enhancing the visual contrast by adaptive tissue attenuation and dynamic range stretching. Using component decomposition and tissue attenuation, a parametric adjustment model was deduced to generate many enhanced images at once. Finally, an ensemble framework was proposed for fusing these enhanced images and producing a high contrast output in both bright and dark regions. 

We have used measurement metrics to evaluate our system and achieved promising scores in each. Moreover, we applied our system to an X-ray data set provided by the Japanese Society of Radiological Technology to help with nodule detection. The experimental results of which demonstrated the effectiveness of our method.