Glaucoma Detection using Tetragonal Local Octa Patterns and SVM from Retinal Images

Abstract

Glaucoma is a fatal disease caused by the imbalance of intraocular pressure inside the eye which can result in lifetime blindness of the victim. Efficient screening systems require experts to manually analyze the images to recognize the disease. However, the challenging nature of the screening method and lack of trained human resources, effective screening-oriented treatment is an expensive task. The automated systems are trying to cope with these challenges; however, these methods are not generalized well to large datasets and real-world scenarios. Therefore, we have introduced an automated glaucoma detection system by employing the concept of the Content-Based Image Retrieval (CBIR) domain. The Tetragonal Local Octa Pattern (T-LOP) is used for features computation which is employed to train the SVM classifier to show the technique significance. We have evaluated our method over challenging datasets namely, Online Retinal Fundus Image (ORIGA) and High-Resolution Fundus (HRF). Both the qualitative and quantitative results show that our technique outperforms the latest approaches due to the effective localization power of T-LOP as it computes the anatomy independent features and ability of Support Vector Machine (SVM) to deal with over-fitted training data. Therefore, the presented technique can play an important role in the automated recognition of glaucoma lesions and can be applied to other medical diseases as well.