Glaucoma is a disease characterized by elevated intraocular pressure (IOP). This increased IOP leads to damage of optic nerve axons at the back of the eye, with eventual deterioration of vision. CDR is a key indicator for the detection of glaucoma. The existing approaches determined the CDR using manual threshold analysis which is fairly time consuming. This paper proposes two methods to extract the disc automatically. The component analysis method and region of interest (ROI) based segmentation are used for the detection of disc. For the cup, component analysis method is used. Later the active contour is used to plot the boundary accurately. This method has been tested on numerous image data sets from Madurai Eye Care Centre, Coimbatore.
Cataract is a condition of the opacity in the lenticular regions, which usually results in bad visual interpretation of the viewed object or any entity. Hence the timely detection of cataract is considered to be significant and can even contribute in the prevention from loss of fight that might occur if the cataract is left untreated. In this paper, detection of cataract disease is carried out based on the image processing technique. Color features, texture features and shape features are extracted separately. This study proposed a Novel Angular Binary Pattern (NABP) for the extraction of texture features. And after the extraction of features, the images are subjected to classification through the implementation of the proposed novel Kernel Based Convolutional Neural Networks. Results are obtained separately for all the three types of features. A comparison is carried out for the proposed work with existing works and based on the results obtained it can be seen that the proposed work comes up with the enhanced results than the traditional methods.
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