CONTENT BASED IMAGE RETRIEVAL: THE FOUNDATION FOR FUTURE CASE-BASED AND EVIEDENCE-BASED OPHTHALMOLOGY

For medical and epidemiologic investigators and caregivers, one powerful functionality yet to be developed is the ability to group retinal images based upon common pathologic appearance. Such a tool would enable advances in evidence-based medicine and would accelerate automated or computer-assisted screening and diagnosis. In this report we show that current, traditional content based image retrieval methods are insufficient to sort dichotomous images (age-related macular degeneration and Stargardt disease) and then propose novel feature extraction techniques that may improve retrieval performance. Prior to processing of the images, a specialized diffusion method to enhance the contrast, reduce the discontinuity, and eliminate edge artifacts is applied to facilitate segmentation. A robust statistic is applied to find abnormal areas and to differentiate AMD from SD. Two methods of analyzing the subretinal deposits are presented - a granulometry based on area morphology and an AM-FM model. Preliminary data show that the image analysis tools show promise as a useful retrieval tool.