Automated change analysis from fluorescein angiograms for monitoring wet macular degeneration.

Detection and analysis of changes from retinal images is important in clinical practice, quantitative scoring of clinical trials, computer-assisted reading centers, and in medical research. This paper presents a fully-automated approach for robust detection and classification of changes in longitudinal time-series of Fluorescein Angiograms (FA). The changes of interest here are related to the development of Choroidal Neo- Vascularization (CNV) in Wet Macular Degeneration. Specif- ically, the changes in CNV regions as well as the Retinal Pigment Epithelium (RPE) hypertrophic regions are detected and analyzed to study the progression of disease and effect of treatment. Retinal features including the vasculature, vessel branch- ing/crossover locations, optic disk and location of the fovea are first segmented automatically. The images are then registered to sub-pixel accuracy using a 12-dimensional mapping that accounts for the unknown retinal curvature and camera pa- rameters. Spatial variations in illumination are removed using a surface fitting algorithm that exploits the segmentations of the various features. The changes are identified in the regions of interest and a Bayesian classifier is used to classify the changes into clinically significant classes. The automated change analysis algorithms were found to have a success rate of 83%.