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

机译：基于超分辨率的临床应用视网膜疾病检测的自动诊断

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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.
机译：在医学图像处理中，病理定位的自动分析和解剖分割步骤更为重要。低分辨率（LR）的眼底不适用于检测视网膜疾病。本文的主要目的是提高从廉价成像装置的低分辨率视网膜图像的分辨率，在较少的计算时间和高精度。因此，我们提出了通过诊断上的超级分辨率和其性能的基底图像（DSA）。该方法仅侧重于感兴趣的区域（ROI）而不是通过减少时间复杂度来集中在整个图像上导致较少的计算时间。因此，使用EIGEN MR间隙特征，能量MR内部特征，香农熵和敏感对比感兴趣（SCI）来捕获所选区域的临床数据。因此，DSA通过使用基于Levenshtein基的KNN分类器来确定。由于分类结果更好，因此在所选区域中采用双子化方法以减少重建误差的损失。通过实验，在MATLAB的平台中执行实现工作，并选择凝视数据库图像。将超分辨率图像性能与诸如PSM，GR-SR，LLE和SPC-SR等不同的技术开始进行比较。最后，收集了低计算超分辨率眼底图像的更高效率。

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