To identify glaucoma type with OCT (optical coherence tomography) images, we present an image processing and machine learning based framework to localize and classify anterior chamber angle (ACA) accurately and efficiently. In digital OCT photographs, our method automatically localizes the ACA region, which is the primary structural image cue for clinically identifying glaucoma type. Next, visual features are extracted from this region to classify the angle as open angle (OA) or angle-closure (AC). This proposed method has three major contributions that differ from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA configurations. Second, it can directly classify ACA as OA/AC based on only visual features, which is different from previous work for ACA measurement that relies on clinical features. Third, it demonstrates that higher dimensional visual features outperform low dimensional clinical features in terms of angle closure classification accuracy. From tests on a clinical dataset comprising of 2048 images, the proposed method only requires 0.26s per image. The framework achieves a 0.921 ± 0.036 AUC (area under curve) value and 84.0% ± 5.7% balanced accuracy at a 85% specificity, which outperforms existing methods based on clinical features.