Glaucoma is a globaleye disease that leads to blindness. This is the second leading cause of vision loss.If it left untreated the patient may lose vision, and even become blind.But blindness from glaucoma can often be prevented with early treatment. Existing Scanning methods like OCT, SLP, HRT has been used for detection of glaucoma but these methods do not identify glaucoma at early stage and also very expensive. Inmost conditions glaucoma gets developed and affects the vision of eye before its detection. In order to avoid this, Image processing techniques has been used for the detection of glaucoma. Image processing are increasingly used in various application such as medical imaging, remote sensing, film industry etc. This proposed work focused on medical image processing. Medical imaging is one of the most powerful tools for gaining insight into normal and pathological processes that affect health. Medical image processing is used for the detection of glaucoma by analyzing fundus images. In the proposed method, the fundus images have been preprocessed and the abnormal features have been extracted using HWT [Haar Wavelet Transform] and GLCM [grey level co-occurrence matrices]. The extracted features are segmented using minimum spanning tree and classified using SVM classifier. The system automatically detect the glaucoma disease of human eye accurately within a seconds from the given fundus image which eliminates the humanerror.
Key words: Glaucoma, Haar Wavelet Transform(HWT),Grey Level CO-occurrence matrices[GLCM], Segmentation, Minimum Spanning Tree[MST],support vector machine [SVM].
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