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An Integrated Approach for the Detection of Glaucoma in Color Fundus Images by using BAT Algorithm

Kuppusamy P. G


An  investigation  of the fundus images acting an essential process in the field of Ophthalmology in finding the ophthalmologic disorders. Most of the eye diseases show off themselves in the retina. This paper therefore focuses on the retinal image analysis and its implications. Among all the eye disorders, Glaucoma is one of the most prevalent causes of blindness and in which the optic nerve is getting damaged due to excessive intraocular pressure. This paper gives  an idea to find the glaucoma disease by segmentation of optic disc and blood vessels using edge and gradient level segmentation algorithm. BAT algorithm is utilized for thresholding of the image.  ANN (Artificial Neural Network) is employed in detecting the presence of glaucoma which involves the training and testing process.  SVM (Support Vector Machine) is applied in order to classify the approximate stage of Glaucoma.  By comparing the normal image and extracted image features like area, the optic disc and blood vessels are identified and measured accurately. The area of glaucoma is measured and from that the stage of Glaucoma is provided. The accuracy attained was 98%.

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