Medical image processing is now gaining a significant momentum in clinical situation to undertake diagnosis of different anatomical defects. However, with regard to eye diseases, there is no such well-defined image processing technique in medical image analysis. The scope of this study is to automate computer analysis of ocular disease-related retinal images, which may ease the job of ophthalmologists to rule out the diseased condition. In this present work, eye images are subjected for developing a reliable tool for processing the eye retinal fundus images. The primary objective is to effectively probe retinal image data for providing a holistic approach in automatic fundus disease detection and screening to help clinicians in addition with a developed reliable image processing technique combined with a rule-based clustering method for automatic analysis of fundus images in a reduced time frame. More than 400 eye images available in online are examined. The images were preprocessed by grayscale conversion, retinal segmentation, ROI and crop ROI, image resizing, and extraction in RGB channels. Then these images were segmented by NRR from RGB channels, centroids of rows and columns, and NRR to binary image conversion. Then extraction of features like cup to disc area, optic cup area, and NRR calculations prior to measuring ISNT. A unique algorithm named as EARMAM was introduced for the prediction of diseased image from healthy eye image pool is envisaged in this paper. The functional significance of the EARMAM algorithm was compared with other common classification algorithm of current practice such as SVM, naïve Bayes, random forest, and SMO. The results of confusion matrix have shown that there was 93% prediction accuracy which was higher than the predictive values of other algorithms. The above results are discussed with future improvement and application in clinical field.

医学图像处理现在在临床情况下获得了显着的发展势头，以进行不同解剖学缺陷的诊断。然而，对于眼部疾病，医学图像分析中并没有这样定义明确的图像处理技术。这项研究的范围是对眼部疾病相关的视网膜图像进行自动计算机分析，这可能会减轻眼科医生排除疾病的工作。在目前的工作中，眼睛图像被用于开发处理眼睛视网膜眼底图像的可靠工具。主要目标是有效地探测视网膜图像数据，为自动眼底疾病检测和筛查提供整体方法，以帮助临床医生，此外还开发可靠的图像处理技术，结合基于规则的聚类方法对眼底图像进行自动分析。减少的时间范围。检查了在线提供的 400 多幅眼睛图像。通过灰度转换、视网膜分割、ROI 和裁剪 ROI、图像大小调整和 RGB 通道提取对图像进行预处理。然后这些图像通过 NRR 从 RGB 通道、行和列的质心以及 NRR 到二进制图像转换进行分割。然后在测量 ISNT 之前提取像杯到椎间盘面积、视杯面积和 NRR 计算等特征。本文提出了一种名为 EARMAM 的独特算法，用于从健康眼图像池中预测病变图像。将 EARMAM 算法的功能意义与当前实践中的其他常见分类算法如 SVM、朴素贝叶斯、随机森林和 SMO 进行了比较。混淆矩阵的结果表明，有93%的预测准确率，高于其他算法的预测值。以上结果与未来在临床领域的改进和应用进行了讨论。混淆矩阵的结果表明，有93%的预测准确率，高于其他算法的预测值。以上结果与未来在临床领域的改进和应用进行了讨论。混淆矩阵的结果表明，有93%的预测准确率，高于其他算法的预测值。以上结果与未来在临床领域的改进和应用进行了讨论。