A Novel Technique for Robust and Fast Segmentation of Corneal Layer Interfaces Based on Spectral-Domain Optical Coherence Tomography Imaging

A novel approach to segment corneal layer interfaces using optical coherence tomography images is presented. In this paper, we performed customized edge detection for initial location of interfaces, fitting the initial interfaces to circles via customized Hough transform, and refining interfaces by employing Kalman filtering to model the horizontal projectile motion of interface boundaries. Validation based on 20 B-scan images from 60 volumes shows that three layer interfaces in each image can be segmented within 0.52 s with an average absolute layer interface error below
$5.4~\mu \text{m}$
. Compared with an existing method, we are able to yield significantly better or similar accuracy at a higher speed with inferior software environment. From the validation experiments based on images from normal human subjects, images with keratoconus and images with laser
in situ
keratomileusis flap, we showed that the proposed customized Hough transform for circles can represent the corneal layer interfaces more accurately. On the other hand, Kalman filtering can handle the heavy noise exhibited in the image, and can be adapted to shape variation in order to be closer to the real-layer interfaces. In conclusion, our approach can be a potential tool to quantify corneal layer interfaces in a clinical environment with lower computational expenses while maintaining high effectiveness.