Premium
Pre‐processing, registration and selection of adaptive optics corrected retinal images
Author(s) -
Ramaswamy Gomathy,
Devaney Nicholas
Publication year - 2013
Publication title -
ophthalmic and physiological optics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.147
H-Index - 66
eISSN - 1475-1313
pISSN - 0275-5408
DOI - 10.1111/opo.12068
Subject(s) - artificial intelligence , computer vision , computer science , wavelet , mathematics , metric (unit) , image registration , rotation (mathematics) , pattern recognition (psychology) , interpolation (computer graphics) , image (mathematics) , operations management , economics
Abstract Purpose In this paper, the aim is to demonstrate enhanced processing of sequences of fundus images obtained using a commercial AO flood illumination system. The purpose of the work is to (1) correct for uneven illumination at the retina (2) automatically select the best quality images and (3) precisely register the best images. Methods Adaptive optics corrected retinal images are pre‐processed to correct uneven illumination using different methods; subtracting or dividing by the average filtered image, homomorphic filtering and a wavelet based approach. These images are evaluated to measure the image quality using various parameters, including sharpness, variance, power spectrum kurtosis and contrast. We have carried out the registration in two stages; a coarse stage using cross‐correlation followed by fine registration using two approaches; parabolic interpolation on the peak of the cross‐correlation and maximum‐likelihood estimation. The angle of rotation of the images is measured using a combination of peak tracking and P rocrustes transformation. Results We have found that a wavelet approach ( D aubechies 4 wavelet at 6th level decomposition) provides good illumination correction with clear improvement in image sharpness and contrast. The assessment of image quality using a ‘ D esigner metric’ works well when compared to visual evaluation, although it is highly correlated with other metrics. In image registration, sub‐pixel translation measured using parabolic interpolation on the peak of the cross‐correlation function and maximum‐likelihood estimation are found to give very similar results ( RMS difference 0.047 pixels). We have confirmed that correcting rotation of the images provides a significant improvement, especially at the edges of the image. We observed that selecting the better quality frames (e.g. best 75% images) for image registration gives improved resolution, at the expense of poorer signal‐to‐noise. The sharpness map of the registered and de‐rotated images shows increased sharpness over most of the field of view. Conclusion Adaptive optics assisted images of the cone photoreceptors can be better pre‐processed using a wavelet approach. These images can be assessed for image quality using a ‘ D esigner M etric’. Two‐stage image registration including correcting for rotation significantly improves the final image contrast and sharpness.