A New Two-Stream Structure Preserving Network for X-Ray Enhancement

Abstract

Generative adversarial network (GAN) is applied in X-ray enhancement, but the tiny structure of X-ray is hard to be maintained by the generator of the GAN. To solve this problem, a new two-stream GAN (named T-S GAN) is proposed to enhance X-rays and guarantee better structure preserving at the same time. In the proposed generator of T-S GAN, the two-stream feature maps are extracted from original and detail X-rays, and then the two-stream features are fused by a coarse-to-fine strategy. Meanwhile, by introducing detail and structure constraints, a mixed loss function is designed for optimizing the T-S GAN. Lastly, to prove the correctness and effectiveness of T-S GAN, a novel dataset of X-ray with finger and wrist fracture is originally created. There are plenty of hairline fractures in the dataset, which can be used to solve the problem of verifying that the tiny structure is preserved by T-S GAN. Experimental results show that the proposed method, compared with the state-of-the-art methods, has lower mean absolute error (MAE), higer peak signal to noise ratio (PSNR) and structure similarity (SSIM) values. A combination of T-S GAN and the state-of-the-art object detection method can be potentially applied for modern medical detection.