Glaucoma is a chronic and irreversible neuro-degenerative disease in which the neuro-retinal nerve that connects the eye to the brain (optic nerve) is progressively damaged and patients suffer from vision loss and blindness. The timely detection and treatment of glaucoma is very crucial to save patient's vision. Computer aided diagnostic systems are used for automated detection of glaucoma that calculate cup to disc ratio from colored retinal images. In this article, we present a novel method for early and accurate detection of glaucoma. The proposed system consists of preprocessing, optic disc segmentation, extraction of features from optic disc region of interest and classification for detection of glaucoma. The main novelty of the proposed method lies in the formation of a feature vector which consists of spatial and spectral features along with cup to disc ratio, rim to disc ratio and modeling of a novel mediods based classier for accurate detection of glaucoma. The performance of the proposed system is tested using publicly available fundus image databases along with one locally gathered database. Experimental results using a variety of publicly available and local databases demonstrate the superiority of the proposed approach as compared to the competitors.
A condition in which the optic nerve inside the eye is swelled due to increased intracranial pressure is known as papilledema. The abnormalities due to papilledema such as opacification of Retinal Nerve Fiber Layer (RNFL), dilated optic disc capillaries, blurred disc margins, absence of venous pulsations, elevation of optic disc, obscuration of optic disc vessels, dilation of optic disc veins, optic disc splinter hemorrhages, cotton wool spots and hard exudates may result in complete vision loss. The ophthalmologists detect papilledema by means of an ophthalmoscope, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound. Rapid development of computer aided diagnostic systems has revolutionized the world. There is a need to develop such type of system that automatically detects the papilledema. In this paper, an automated system is presented that detects and grades the papilledema through analysis of fundus retinal images. The proposed system extracts 23 features from which six textural features are extracted from Gray-Level Co-occurrence Matrix (GLCM), eight features from optic disc margin obscuration, three color based features and seven vascular features are extracted. A feature vector consisting of these features is used for classification of normal and papilledema images using Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. The variations in retinal blood vessels, color properties, texture deviation of optic disc and its peripapillary region, and fluctuation of obscured disc margin are effectively identified and used by the proposed system for the detection and grading of papilledema. A dataset of 160 fundus retinal images is used which is taken from publicly available STARE database and local dataset collected from Armed Forces Institute of Ophthalmology (AFIO) Pakistan. The proposed system shows an average accuracy of 92.86% for classification of papilledema and normal images. It also shows an average accuracy of 97.85% for classification of already classified papilledema images into mild and severe papilledema. The proposed system is a novel step towards automated detection and grading of papilledema. The results showed that the technique is reliable and can be used as clinical decision support system.
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