Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs

Kensuke Umehara; Junko Ota; Naoki Ishimaru; Shunsuke Ohno; Kentaro Okamoto; Takanori Suzuki; Naoki Shirai; Takayuki Ishida

doi:10.1117/12.2249969

24 February 2017 Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs

Kensuke Umehara, Junko Ota, Naoki Ishimaru, Shunsuke Ohno, Kentaro Okamoto, Takanori Suzuki, Naoki Shirai, Takayuki Ishida

Author Affiliations +

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101331P (2017) https://doi.org/10.1117/12.2249969
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Kensuke Umehara, Junko Ota, Naoki Ishimaru, Shunsuke Ohno, Kentaro Okamoto, Takanori Suzuki, Naoki Shirai, and Takayuki Ishida "Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331P (24 February 2017); https://doi.org/10.1117/12.2249969

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