Abstract
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.
Highlights
Chest radiography is the most commonly performed diagnostic imaging technique for identifying various pulmonary diseases, including lung nodules, pneumonia, and pneumoconiosis
We previously demonstrated that the use of the super-resolution convolutional neural network (SRCNN) scheme has the potential to provide an effective approach for improving image resolution in chest radiographs [12]
A total of 247 chest radiographs were sampled from the JSRT Database, which is an open-access database created by the Japanese Society of Radiological Technology [13]
Summary
Chest radiography is the most commonly performed diagnostic imaging technique for identifying various pulmonary diseases, including lung nodules, pneumonia, and pneumoconiosis. When radiologists need to verify small diagnostic signals such as lung nodules on an image, they enlarge the region-of-interest (ROI) using well-established linear interpolation methods. Such methods are commonly used for improving image resolution of a low-resolution image to generate a high-resolution image. The single image super-resolution method is the post-processing approach for reconstructing a high-resolution image from a low-resolution image, and can greatly reduce artifacts resulting from linear interpolation methods. Recent super-resolution methods are example-based methods that learn the relationship between low-resolution and high-resolution image pairs. The sparse-coding super-resolution (ScSR) scheme [2] [3] is the archetypal example-based super-resolution method. Previous studies demonstrated the superiority of the ScSR method over conventional linear interpolation methods in the image quality of medical images [4] [5]
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