Abstract: “Glaucoma is the second most common cause of blindness. It is caused by high intraocular pressure within the eyes, resulting in an injury to the optic nerve. Currently, there is no cure for glaucoma. However, early detection and treatment can prevent disease progression. Thus, the use of automatic glaucoma screening system can help workload of healthcare professionals in early detection and also solve cost issues. This study proposes a method for identifying glaucoma from fundus images by using a fusion three features to find glaucoma’s significant by using wavelet decomposition and texture such as Discrete Wavelet Transform (DWT), Principal Components Analysis (PCA), and Local Binary Patterns (LBP). Support vector machine (SVM) is used to classify the glaucoma condition. The experimental results yield high accuracy at 95% by using tenfold cross-validation with HRF Database and using fusion three feature as DWT, PCA, and LBP.”

Keywords: Glaucoma, Fundus Image, Data Mining, Feature Extraction, Feature Ranking, Classification