Abstract
Single image super-resolution (SR) method can generate a high-resolution (HR) image from a low-resolution (LR) image
by enhancing image resolution. In medical imaging, HR images are expected to have a potential to provide a more
accurate diagnosis with the practical application of HR displays. In recent years, the super-resolution convolutional
neural network (SRCNN), which is one of the state-of-the-art deep learning based SR methods, has proposed in
computer vision. In this study, we applied and evaluated the SRCNN scheme to improve the image quality of magnified
images in chest radiographs. For evaluation, a total of 247 chest X-rays were sampled from the JSRT database. The 247
chest X-rays were divided into 93 training cases with non-nodules and 152 test cases with lung nodules. The SRCNN
was trained using the training dataset. With the trained SRCNN, the HR image was reconstructed from the LR one. We
compared the image quality of the SRCNN and conventional image interpolation methods, nearest neighbor, bilinear and
bicubic interpolations. For quantitative evaluation, we measured two image quality metrics, peak signal-to-noise ratio
(PSNR) and structural similarity (SSIM). In the SRCNN scheme, PSNR and SSIM were significantly higher than those
of three interpolation methods (p<0.001). Visual assessment confirmed that the SRCNN produced much sharper edge
than conventional interpolation methods without any obvious artifacts. These preliminary results indicate that the
SRCNN scheme significantly outperforms conventional interpolation algorithms for enhancing image resolution and that
the use of the SRCNN can yield substantial improvement of the image quality of magnified images in chest radiographs.
