Motion distortion correction in scanning ophthalmoscopy by iterative image registration

Abstract

 

Purpose

 

Correction of distortion due to eye motion in scanning ophthalmoscope images is critical for multiple applications, including: increasing signal-to-noise ratio and/or speckle reduction through averaging, creating perfusion maps using motion contrast techniques and registration for longitudinal studies. Here, we demonstrate the improvement of a registration algorithm by applying it iteratively.

 

Methods

 

An image registration algorithm (Dubra & Harvey, Lect. Notes Comput. Sc. 6204, pp. 60-71) that estimates motion by comparing image strips against a reference frame using a normalized cross correlation (NCC), was modified to: perform multiple iterations, achieve sub-pixel accuracy through NCC interpolation, and account for line skew. The algorithm was tested on image sequences from a confocal adaptive optics (A O) scanning light ophthalmoscope (SLO). Average NCC values of 30 images and the average strip displacement after registration were used as performance metrics.

 

Results

 

Results from subjects with normal fixation (achromatopsia and controls) show that most of the quantitative and qualitative improvements take place in the first three iterations (figures 1 and 2 respectively). Actual values vary substantially across image sequences, but NCC increases in the order of 1.5% for iterative registration alone were measured, with an additional 0.5% when incorporation sub-pixel NCC maximum estimation and less than 0.2% due to skew correction. Sources of image variability such as tear film evaporation, poor and/or slow AO correction and electronics noise prevented achieving NCC values of one.

 

Conclusions

 

Skew correction appears negligible in subjects with normal fixation, and it is expected to provide its most benefit in slower scanning modalities such as optical coherence tomography or in subjects with nystagmus. Both iterative registration and sub-pixel motion estimation translate in measurable improvement of the selected metrics, with their combination providing the best performance.

 

 

Region showing of registered image after iterations 1 through 4 (A - D). Iteration 4 using iterative correction (E), skew correction (F), sub-pixel motion correction (G), and sub-pixel motion correction with skew correction (H)

 

 

Magnitude of motion correction (top) and average NCC value across all frames (bottom) in an AO SLO image sequence across iterations

 

Keywords: 549 image processing • 552 imaging methods (CT, FA, ICG, MRI, OCT, RTA, SLO, ultrasound) • 551 imaging/image analysis: non-clinical