Super-Resolution Based Automatic Diagnosis of Retinal Disease Detection for Clinical Applications

摘要

In medical image processing, the automatic analysis of pathology localization and the anatomical segmentation steps are more important. The Fundus images of Low resolution (LR) are not applicable to detect the retinal disease. The main aim of this paper is to enhance the resolution of the low-resolution retinal images obtained from the cheap imaging devices within less computational time and high accuracy. So, we proposed the fundus image with Super-Resolution and its performance via the Diagnostically Significant Area (DSA). This approach focuses only on the region of Interest (ROI) instead of concentrating on the entire image leading to less computational time by reducing the time complexity. Therefore, the Eigen MR inter-band feature, Energy MR intra-band feature, Shannon entropy and Sensitive Contrast Interest (SCI) are used to capture the clinical data from the selected region. Therefore, the DSA is determined by using Levenshtein based KNN classifier. Because of better classification outcomes, the Bicubic method is employed in the selected region to reduce the loss of reconstruction error. Experimentally, the implementation works are carried out in the platform of MATLAB with DRIVE and STARE database images are chosen. The super-resolution image performances are compared with different start of art techniques such as PSM, GR-SR, LLE, and SpC-SR. Finally, higher efficiency with low computational super-resolution fundus images is collected.