Kestrel proposes a Stereoscopic Grading System for Age-related Maculopathy (ARMD) using affordable stereoscopic visualization devices and modern computer interfaces. Our objective is to develop a computer- based system for grading retinal images which display volumetric lesions, and to increase efficiency and quantitativeness while reducing reader variability. Revolutionary advances in technology make it possible to move exiting """"""""light-table and hand-held stereoscopic viewers"""""""" to the computer screen. This system will have immediate impact by reducing the cost of grading large volumes of patient data. Ultimately, the proposed system will be a significant factor, through its application in epidemiologic studies which attempt to identify causal effects of ARMD. Kestrel's research approach involves (I) Automating grading activity using color data from digitized stereoscopic images, while moving the burden of counting and measuring lesions from the grader to the computer. (2) Analyzing stereoscopic photographs by generating topographical maps for input into a neural network which will detect and classify ARMD-related pathologies. The innovations include: (1) Explicit 3-D disparity maps generation. (2) Current 3-D technology as an alternative to manual grading. (3) Speech recognition interface for instruction sand data entry. (4) Neural network algorithms enhance feature segmentation and classification.
Development of a commercial stereoscopic system for grading retinal images allows the precise measurement and quantification of pathologies for statistical analyses. The system will have an immediate impact on epidemiological studies by reducing the cost of grading large populations of patient images. As a quantitative tool for determining the efficacy of new procedures for treating ARMD, the Stereoscopic Grading System for ARMD will be offered to pharmaceuticals and researchers.
