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Automatic Optic Disc Localization and Optic Cup Segmentation from Monocular Color Retinal Images for Glaucoma Assessment

机译:从单眼彩色视网膜图像自动视盘定位和视杯分割,以评估青光眼

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In recent years, computer-aided screening and diagnosis of glaucoma have made considerable progress. Computer-aided retinal image analysis provides an instant detection of retinal features before specialist inspection. Eye disease of glaucoma is a significant globally is due to an increase in intraocular pressure. It causes permanent vision loss, and also early prognosis is complicated. The present work provides an image processing technique used to automatically detect the center of the optic disc and segment the optic cup for prediction of glaucoma by developing software algorithms in MATLAB. Three kinds of steps are used such as pre-processing, detection of optic disc center and optic cup. In the first step, green Chanel image and filtering methods used for removing noises. In the second step, Region localization using entropy is applied to locate Optic Disc center. In the third step, the Optic Cup has segmented by region growing technique. This subject to different retinal fundus image datasets such as DRIONS and DRIVE. Proposed algorithm is obtained 100% Accuracy rate(A), 98% Sensitivity(S), 99% Specificity(Sp), 95% Precision(P), 87% F-score(Fs), 94% G-mean(Gm) and trivial computation time.
机译:近年来,计算机辅助青光眼的筛查和诊断取得了长足的进步。计算机辅助的视网膜图像分析可在专科医生检查之前立即检测出视网膜特征。青光眼的眼部疾病在全球范围内是重要的,这是由于眼内压升高引起的。它会导致永久性视力丧失,并且早期预后也很复杂。本工作提供了一种图像处理技术,该技术可通过在MATLAB中开发软件算法来自动检测视盘的中心并分割视杯以预测青光眼。使用三种步骤,例如预处理,视盘中心检测和视杯。第一步,使用绿色香奈儿图像和滤波方法去除噪声。在第二步中,应用使用熵的区域定位来定位光盘中心。第三步,将视杯按区域生长技术进行细分。这受制于不同的视网膜眼底图像数据集,例如DRIONS和DRIVE。提出的算法获得了100%的准确率(A),98%的灵敏度(S),99%的特异性(Sp),95%的精度(P),87%的F得分(Fs),94%的G均值(Gm)和微不足道的计算时间。

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