Tissue-Specific Analysis of Super Resolution Methods for Medical Images

Abstract

Image super-resolution (SR) techniques are widely used in various domains toenhance the resolution of low-resolution images, producing visually appealinghigh-resolution versions. However, regarding medical images, SR methods mustproduce precise results. Therefore, a thorough evaluation of the performance ofdifferent SR methods on various tissues is essential to determine their suitability.In particular, evaluating SR methods on region-specific organs, such as thelung, liver, and kidney in CT scans and brain in MRI scans, is essential. Whenthese organs are individually enhanced using Bi-cubic interpolation and Modified-ESPCN methods, along with standard evaluation metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), it is observed that SRmethods exhibit inferior performance on most individual regions of interest comparedto the entire image. This difference in performance can lead to misleadinglyhigh results when evaluated over the entire image, which includes irrelevant nontissueregions.We propose using a tissue-specific model incorporating a region-based lossfunction to overcome this limitation. This approach allows for a more accurateand informative evaluation of SR methods in the context of tissue-specific performanceanalysis for CT images.