Modeling of the rhodopsin bleaching with variational analysis of retinal images

This paper discusses a variational method of processing the scanning laser ophthalmoscope(cSLO) image sequences in the context of extracting the local rhodopsin density and modeling the bleaching kinetics. This work supports the characterization and detection of early pathological changes in clinical retinal data. Our goals include providing automated tools for tracing early pathological changes over time, in particular rhodopsin density variations and local lesion progression. Our computational approach is a variational technique that approximates measured cSLO image sets optimally within the range of the bleaching model. The characterizing parameters of the approximating curves are computed locally and their spatial changes reflect variations in bleaching kinetics and hence changes in the local rhodopsin density. The curve fitting in the temporal direction of the image stack can be also viewed as a denoising/enhancement routine. The advantages of the temporal correction include a better fit of the image intensity function to the model and the avoidance of local averaging that would impair the spatial resolution.