Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping

TL;DR

This paper introduces an interpretable deep learning method for enhancing X-ray images by automatically adjusting brightness and contrast, providing explanations for the enhancements, and demonstrating consistent improvements on clinical datasets.

Contribution

The proposed method is the first to combine automatic image enhancement with interpretability through pixel maps, inspired by clinical workflows.

Findings

Achieved 24.75 dB PSNR in image enhancement

Attained 0.8431 SSIM indicating high structural similarity

Provided interpretable pixel maps for explanation

Abstract

X-ray imaging is the most widely used medical imaging modality. However, in the common practice, inconsistency in the initial presentation of X-ray images is a common complaint by radiologists. Different patient positions, patient habitus and scanning protocols can lead to differences in image presentations, e.g., differences in brightness and contrast globally or regionally. To compensate for this, additional work will be executed by clinical experts to adjust the images to the desired presentation, which can be time-consuming. Existing deep-learning-based end-to-end solutions can automatically correct images with promising performances. Nevertheless, these methods are hard to be interpreted and difficult to be understood by clinical experts. In this manuscript, a novel interpretable mapping method by deep learning is proposed, which automatically enhances the image brightness and contrast globally and locally. Meanwhile, because the model is inspired by the workflow of the brightness and contrast manipulation, it can provide interpretable pixel maps for explaining the motivation of image enhancement. The experiment on the clinical datasets show the proposed method can provide consistent brightness and contrast correction on X-ray images with accuracy of 24.75 dB PSNR and 0.8431 SSIM.