Elsharkawy Mohamed, Sharafeldeen Ahmed, Soliman Ahmed, Khalifa Fahmi, Ghazal Mohammed, El-Daydamony Eman, Atwan Ahmed, Sandhu Harpal Singh, El-Baz Ayman
Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.
Electrical and Computer Engineering Department, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates.
Diagnostics (Basel). 2022 Feb 11;12(2):461. doi: 10.3390/diagnostics12020461.
Early diagnosis of diabetic retinopathy (DR) is of critical importance to suppress severe damage to the retina and/or vision loss. In this study, an optical coherence tomography (OCT)-based computer-aided diagnosis (CAD) method is proposed to detect DR early using structural 3D retinal scans. This system uses prior shape knowledge to automatically segment all retinal layers of the 3D-OCT scans using an adaptive, appearance-based method. After the segmentation step, novel texture features are extracted from the segmented layers of the OCT B-scans volume for DR diagnosis. For every layer, Markov-Gibbs random field (MGRF) model is used to extract the 2nd-order reflectivity. In order to represent the extracted image-derived features, we employ cumulative distribution function (CDF) descriptors. For layer-wise classification in 3D volume, using the extracted Gibbs energy feature, an artificial neural network (ANN) is fed the extracted feature for every layer. Finally, the classification outputs for all twelve layers are fused using a majority voting schema for global subject diagnosis. A cohort of 188 3D-OCT subjects are used for system evaluation using different -fold validation techniques and different validation metrics. Accuracy of 90.56%, 93.11%, and 96.88% are achieved using 4-, 5-, and 10-fold cross-validation, respectively. Additional comparison with deep learning networks, which represent the state-of-the-art, documented the promise of our system's ability to diagnose the DR early.
糖尿病视网膜病变（DR）的早期诊断对于抑制视网膜的严重损伤和/或视力丧失至关重要。在本研究中，提出了一种基于光学相干断层扫描（OCT）的计算机辅助诊断（CAD）方法，用于利用视网膜结构的三维扫描早期检测DR。该系统使用先验形状知识，通过一种基于外观的自适应方法自动分割三维OCT扫描的所有视网膜层。在分割步骤之后，从OCT B扫描体积的分割层中提取新的纹理特征用于DR诊断。对于每一层，使用马尔可夫-吉布斯随机场（MGRF）模型提取二阶反射率。为了表示提取的图像衍生特征，我们采用累积分布函数（CDF）描述符。对于三维体积中的逐层分类，利用提取的吉布斯能量特征，将每一层提取的特征输入人工神经网络（ANN）。最后，使用多数投票方案融合所有十二层的分类输出，以进行全局受试者诊断。使用188名三维OCT受试者组成的队列，采用不同的交叉验证技术和不同的验证指标对系统进行评估。分别使用4折、5折和10折交叉验证时，准确率分别达到90.56%、93.11%和96.88%。与代表当前技术水平的深度学习网络进行的额外比较，证明了我们的系统早期诊断DR能力的前景。
