论文标题
分解用于骨骼和软组织的X射线图像
Decompose X-ray Images for Bone and Soft Tissue
论文作者
论文摘要
骨骼总是用软组织包裹。结果，其X射线图像中的骨骼被遮盖并不清楚。在本文中，我们解决了这个问题，并提出了一项新的任务，以通过图像处理算法虚拟分解软组织和骨头。该任务与分割根本不同，因为分解的图像共享相同的成像域。我们的分解任务也与常规图像增强根本不同。我们为这种分解提出了一个新的数学模型。我们的模型不适合，因此需要一些先验。通过适当的假设，可以通过求解标准拉普拉斯方程来解决我们的模型。从理论上讲，所得的骨骼图像比原始输入图像具有更好的对比度。因此，骨骼的细节得到了增强并变得更加清晰。几个数值实验证实了我们方法的有效和效率。我们的方法对于临床诊断，手术计划，识别，深度学习等很重要。
Bones are always wrapped by soft tissues. As a result, bones in their X-ray images are obscured and become unclear. In this paper, we tackle this problem and propose a novel task to virtually decompose the soft tissue and bone by image processing algorithms. This task is fundamentally different from segmentation because the decomposed images share the same imaging domain. Our decomposition task is also fundamentally different from the conventional image enhancement. We propose a new mathematical model for such decomposition. Our model is ill-posed and thus it requires some priors. With proper assumptions, our model can be solved by solving a standard Laplace equation. The resulting bone image is theoretically guaranteed to have better contrast than the original input image. Therefore, the details of bones get enhanced and become clearer. Several numerical experiments confirm the effective and efficiency of our method. Our approach is important for clinical diagnosis, surgery planning, recognition, deep learning, etc.
