Detection of glaucoma in retinal image using support vector machine.

Glaucoma disease causes the loss of eye sight if unnoticed. Earlier detection and the extent to which the disease is progressed may provide good suggestions to the physicians for giving treatment. In this work the glaucoma severity stage with its disease types are understood the image processing techniques. In this work, an image segmentation algorithm, namely, OTSU's segmentation method is chosen. It detects optic cup (OC) and optic disc (OD). Several researchers found only Cup-to-Disk ratio (CDR). CDR did not apply to categorize the images whether they are normal or affected by glaucoma. So it is decided that calculating rim-to-disk ratio (RDR) may give an expected solution in assessing the glaucoma disease. The classifiers are used to find the progression of disease. In this research Support vector machine (SVM) is chosen as the classifier. The abnormal images are of four stages. They are Inferior, Superior, Nasal, Temporal and the types are found out by ISNT method. Further this work concludes the disease type by indicating either diabetic retinopathy or glaucoma. [ABSTRACT FROM AUTHOR]