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.
Abstract (translated)
X射线成像是最广泛使用的医学影像模式。然而，在常见实践中，放射科医生常常抱怨初始呈现的X射线图像存在不一致性问题。不同的患者体位、体型以及扫描协议会导致图像亮度和对比度上的差异，无论是全局还是局部都可能出现这样的变化。为了弥补这一点，临床专家需要额外的工作来调整图像以达到理想的展示效果，这可能非常耗时。现有的基于深度学习的端到端解决方案能够自动修正这些图像，并表现出令人鼓舞的效果。然而，这些方法难以解释和理解，对于临床专家来说较为复杂。 在本文献中，提出了一种新的可解释性映射方法，利用深度学习技术来自动增强X射线图像的整体及局部亮度和对比度。同时，由于该模型的设计灵感来源于亮度与对比度调整的工作流程，它可以提供可解释的像素图来说明图像增强的动力机制。 在临床数据集上的实验表明，所提出的方法可以在X射线图像上提供一致性的亮度和对比度校正，并且其性能指标达到了24.75 dB PSNR（峰值信噪比）及0.8431 SSIM（结构相似性指数）。
URL
https://arxiv.org/abs/2501.12245
