Aligning simulated and imaged spectral retinal data for model personalisation

Abstract

The aim of this thesis is to show the need for alignment of image and simulation model spectra when applying inversion to analyze spectral retinal images. This is done by generating parametric concentration maps and visually analyzing them. The alignment process begins by segmenting subsets of the image and simulation model spectra. In this thesis, the macular pigment region is used. The transformations needed to align these subsets are then calculated by utilizing a method based on principal component analysis. The whole simulation model spectra is then transformed with the computed transformations to produce a personalized model for a given spectral retinal image. Parametric concentration maps are then generated using inversion. In inversion, a k-nearest neighbor algorithm is used to find the closest spectral value from the simulation model for every spatial location in the spectral retinal image. Then the five parameter values used to calculate that particular simulation model spectra can be extracted and five concentration maps can be created after this process is applied to every spatial location in the spectral retinal image.