We explore methods of segmenting bone structure from normal chest X-ray radiographs to obtain virtual dual-energy X-ray radiographs of soft tissue. Our goal is to obtain high-quality clinical radiographs without increasing the radiation dose. Our algorithm first divides the lung field into 8 sections:upper

middle

and lower part of the left and right lateral lobe and left lung. The lung has specific anatomical structures in each section. For every section

we use standard chest radiographs and the corresponding bone image to conduct large-scale training of artificial neural networks (ANN). After training

we acquire the virtual bone image of this section with ANN

and obtain a complete image by fusing these 8 images. We use minimal total variation to suppress the noise in the image and enhance the edge of the bone. Finally

we subtract the virtual bone image from the original image to obtain the virtual soft tissue image. To test our algorithm

we use 100 radiographs with nodules. This new method can be applied to normal radiographs. After processing

we obtain a virtual soft tissue image

in which the bone structure is effectively removed. The virtual soft tissue can preserve lung nodules and blood vessels

and is helpful for diagnosing pulmonary nodules. Our novel method can improve lung nodule recognition rate to 88% (traditional rate is 70%). Based on anatomical structures

artificial neural networks and regression models can effectively isolate bones. This method can be widely applied in clinical diagnosis

to help radiologists detect lung nodules.