Author(s)

Kensuke Umehara, Junko Ota, Takayuki Ishida

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

Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p < 0.001). The SRCNN-processed image quality in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases were significantly higher than that in low-density breasts, low-risk BI-RADS assessment groups, and benign cases, respectively (p < 0.01). Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.

Super-Resolution, Deep-Learning, Artificial Intelligence, Breast Imaging Reporting and Data System (BI-RADS), Mammography

Umehara, K. , Ota, J. and Ishida, T. (2017) Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network. Open Journal of Medical Imaging, 7, 180-195. doi: 10.4236/ojmi.2017.74018.