Glaucoma is a critical retinal disorder due to high intraocular pressure (IOP) within the eye. It causes irreversible damage of the optical nerve head (ONH). The available glaucoma detection methods using decomposition techniques with gray images or only green channel images are less accurate. In this paper, a more accurate method for glaucoma detection using image channels (ICs) and discrete wavelet transform (DWT) from fundus images is proposed. Firstly, input images are resized then red channel (RC), green channel (GC), blue channel (BC), and gray scale (Gs) images are extracted. Secondly, these four types of images are enhanced and decomposed separately into sub band images (SBIs) using second level (SL) DWT. Thirdly, most useful features are extracted from each of DWT SBIs. Fourthly, extracted features from each of RC, GC, BC, and Gs images are concatenated and normalized. Finally, robust features are selected and fed to the least square support vector machine (LS-SVM) classifier. The obtained glaucoma detection accuracy of the proposed method is 84.95% which is more than the existing methods using the same image database. The proposed method may become suitable for ophthalmologists to detect glaucoma with better accuracy.

青光眼是由于眼内高眼压 (IOP) 而导致的严重视网膜疾病。它会对视神经乳头 (ONH) 造成不可逆的损伤。使用灰度图像或仅绿色通道图像的分解技术的可用青光眼检测方法不太准确。在本文中，提出了一种使用眼底图像的图像通道 (IC) 和离散小波变换 (DWT) 进行青光眼检测的更准确方法。首先，调整输入图像的大小，然后提取红色通道 (RC)、绿色通道 (GC)、蓝色通道 (BC) 和灰度 (Gs) 图像。其次，使用二级 (SL) DWT 将这四种类型的图像分别增强和分解为子带图像 (SBI)。第三，从每个 DWT SBI 中提取最有用的特征。第四，分别从 RC、GC、BC 中提取特征，和 Gs 图像被连接并归一化。最后，选择稳健的特征并将其馈送到最小二乘支持向量机 (LS-SVM) 分类器。该方法获得的青光眼检测准确率为84.95%，优于使用相同图像数据库的现有方法。所提出的方法可能适合眼科医生更准确地检测青光眼。