Glaucoma Detection using Tetragonal
Local Octa Patterns and SVM from Retinal Images
Marriam
Nawaz, Tahira Nazir, and Momina Masood
Department of Computer Science, University of Engineering
and Technology, Pakistan
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.
Keywords: Retinal images, glaucoma, SVM, classification.
Received
April 28, 2020; accepted November 24, 2020