Author(s)

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

Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan.

Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image quality of magnified chest radiographs. Materials and Methods: A total of 247 chest X-rays were sampled from the JSRT database, then divided into 93 training cases with non-nodules and 154 test cases with lung nodules. We first trained two types of super-resolution methods, sparse-coding super-resolution (ScSR) and super-resolution convolutional neural network (SRCNN). With the trained super-resolution methods, the high-resolution image was then reconstructed using the super-resolution methods from a low-resolution image that was down-sampled from the original test image. We compared the image quality of the super-resolution methods and the linear interpolations (nearest neighbor and bilinear interpolations). For quantitative evaluation, we measured two image quality metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For comparative evaluation of the super-resolution methods, we measured the computation time per image. Results: The PSNRs and SSIMs for the ScSR and the SRCNN schemes were significantly higher than those of the linear interpolation methods (p < 0.001 or p < 0.05). The image quality differences between the super-resolution methods were not statistically significant. However, the SRCNN computation time was significantly faster than that of ScSR (p < 0.001). Conclusion: Super-resolution methods provide significantly better image quality than linear interpolation methods for magnified chest radiograph images. Of the two tested schemes, the SRCNN scheme processed the images fastest; thus, SRCNN could be clinically superior for processing radiographs in terms of both image quality and processing speed.

Deep Learning, Super-Resolution, Super-Resolution Convolutional Neural Network (SRCNN), Sparse-Coding Super-Resolution (ScSR), Chest X-Ray

Umehara, K. , Ota, J. , Ishimaru, N. , Ohno, S. , Okamoto, K. , Suzuki, T. and Ishida, T. (2017) Performance Evaluation of Super-Resolution Methods Using Deep-Learning and Sparse-Coding for Improving the Image Quality of Magnified Images in Chest Radiographs. Open Journal of Medical Imaging, 7, 100-111. doi: 10.4236/ojmi.2017.73010.