Data Augmentation of CT Images of Liver Tumors to Reconstruct Super-Resolution Slices based on a Multi-Frame Approach
Moslem Farhadi, Amir Hossein Foruzan · 2019
Improvement of tumor images have applications in the extraction of features and image retrieval algorithms. The multi-frame super-resolution technique is a chief approach in high-resolution image reconstruction. However, the acquisition of several low-resolution frames is not practical in the medical domain. In this paper, we use volumetric CT data of the abdominal region to prepare new low-resolution images and employ them in a data augmentation approach to the super-resolution image reconstruction. We showed that our method improved the results of interpolation and conventional multi-frame approaches by 0.02 and 0.03 respectively using the SSIM index.