Decompose X-ray Images for Bone and Soft Tissue

TL;DR

This paper introduces a novel mathematical approach to virtually decompose X-ray images into bone and soft tissue components, enhancing bone visibility for medical applications.

Contribution

A new decomposition model based on solving a Laplace equation that improves bone contrast in X-ray images, differing from segmentation and enhancement methods.

Findings

The method effectively enhances bone details in X-ray images.

Numerical experiments confirm the approach's efficiency and effectiveness.

The model guarantees improved contrast for bone images.

Abstract

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.