Fundus image change analysis: Geometric and radiometric normalization

Image change analysis will potentiate fundus feature quantitation in natural history and intervention studies for major blinding diseases such as age-related macular degeneration and diabetic retinopathy. Geometric and radiometric normalization of fundus images acquired at two points in time are required for accurate change detection, but existing methods are unsatisfactory for change analysis. We have developed and explored algorithms for correction of image misalignment (geometric) and inter- and intra-image brightness variation (radiometric) in order to facilitate highly accurate change detection. Thirty-five millimeter color fundus photographs were digitized at 500 to 1000 dpi. Custom-developed registration algorithms correcting for translation only; translation and rotation; translation, rotation, and scale; and polynomial based image-warping algorithms allowed for exploration of registration accuracy required for change detection. Registration accuracy beyond that offered by rigid body transformation is required for accurate change detection. Radiometric correction required shade-correction and normalization of inter-image statistical parameters. Precise geometric and radiometric normalization allows for highly accurate change detection. To our knowledge, these results are the first demonstration of the combination of geometric and radiometric normalization offering sufficient accuracy to allow for accurate fundus image change detection potentiating longitudinal study of retinal disease.