Abstract
Purpose :
Adaptive-optics scanning laser ophthalmoscopy (AOSLO) records line-by-line video sequences that are subject to motion artifacts. Strip alignment cross-correlation has been the procedure of choice to correct for eye motion. The procedure requires human interaction in finding a reference frame. We are investigating the use of automated cross-correlation to located a suitable reference frame. We also investigated the influence of strip width and strip spacing on the success of the strip alignment cross-correlation.
Methods :
AOSLO video sequences were recorded with our custom-built instrument using the design by Dubra [Dubra Biomedical Optics Express (2011)]. Images were exported to a workstation with nVidia multicore graphics card running Matlab and NIH ImageJ. We used a set of 50 video sequences taken from the AOSLO. We used automated cross-correlation algorithm (ACCA) (StackReg plugin to NIH ImageJ) to analyze the correlation between subsequent frames. The maximum ACCA was used a screening tool to increase the detection speed of the optimal reference frame for strip alignment. We compared the result of NIH ImageJ plugin to the decision by a trained operator. For the strip width evaluation we varied the strip width between 8 and 40 pixel in steps of 8. For the strip spacing evaluation we varied the spacing between 8 and 40 pixel in steps of 8.
Results :
We found that ACCA increased the speed by finding good candidates for a toggle view by a human operator. The strip width and spacing variation had little influence on the resulting size of the image. The most important variable was image quality of the video sequence.
Conclusions :
AOSLO is limited in its clinical application by the low yield and long post-acquisition processing time. , it is estimated that for every hour of imaging between 10-20 hours of processing is needed. This severely limited the ability of clinicians to use AOSLO imaging on a routine basis. The high-resolution of the instrument limits the field-of-view. The small field-of-view entails that a large number of images have to acquired to yield a reasonable mosaic of overlapping individual images. In addition, light safely limits the quality of a single frame making it necessary to acquire a series of sequential frames at the same location. Continued advances in image processing and hardware motion correction are required to transform the operation of AOSLO imaging into a routine clinical device.
This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.