Performance Analysis of Gray Level Co- Occurrence Matrix Texture Features for Glaucoma Diagnosis

Glaucoma is a multifactorial optic neuropathy disease characterized by elevated Intra Ocular Pressure (IOP). As the visual loss caused by the disease is irreversible, early detection is essential. Fundus images are used as input and it is preprocessed using histogram equalization. First order features from histogram and second order features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the preprocessed image as textural features reflects physiological changes in the fundus images. Second order textural features are extracted for different quantization levels namely 8, 16, 32, 64, 128 and 256 in four orientations viz 0, 45, 90 and 135° for various distances. Extracted features are selected using Sequential Forward Floating Selection (SFFS) technique.The selected features are fed to Back Propagation Network (BPN) for classification as normal and abnormal images. The proposed computer aided diagnostic system achieved 96% sensitivity, 94% specificity, 95% accuracy and can be used for screening purposes. In this study, the analysis of gray levels have shown their significance in the classification of glaucoma.