Local versus Global Features for Content-Based Image Retrieval
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Effects of preprocessing eye fundus images on appearance based glaucoma classification
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Classifying glaucoma with image-based features from fundus photographs
Proceedings of the 29th DAGM conference on Pattern recognition
Optic disk and cup boundary detection using regional information
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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Glaucoma is an eye disorder that causes irreversible loss of vision and is prevalent in the aging population. Glaucoma is indicated both by structural changes and presence of atrophy in retina. In retinal images, these appear in the form of subtle variation of local intensities. These variations are typically described using local shape based statistics which are prone to error. We propose an automated, global feature based approach to detect glaucoma from images. An image representation is devised to accentuate subtle indicators of the disease such that global image features can discriminate between normal and glaucoma cases effectively. The proposed method is demonstrated on a large image dataset annotated by 3 medical experts. The results show the method to be effective in detecting subtle glaucoma indicators. The classification performance on a dataset of 1186 color retinal images containing a mixture of normal, suspect and confirmed cases of glaucoma is 97 percent sensitivity at 87 percent specificity. This improves further when the suspect cases are removed from the abnormal cases. Thus, the proposed method offers a good solution for glaucoma screening from retinal images.