Retinal Image Registration with Haar-Optimized Local Binary Descriptors for Bifurcation Points

This paper introduces a novel method for the registration of color fundus photographs, featuring a new descriptor named Haar-Optimized Local Binary Descriptor (HOLBD). HOLBD is a fast-to-compute and match descriptor, highly optimized to uniquely describe retinal bifurcation and crossover points, which are crucial landmarks for fundus image registration. It utilizes four patterns reminiscent of Haar basis functions, optimized to define these bifurcation and crossover points. These patterns perform pixel intensity tests to form a 340-bit binary vector. Before computing the HOLBD descriptor, the overall image orientation and scaling factors are estimated, and images are normalized, making HOLBD robust against rotation and scaling. Experiments were conducted on both publicly available and private retinal image registration datasets, comprising a total of 484 retinal images (i.e., 242 pairs). The proposed method was compared with state-of-the-art techniques, including Generalized Dual-Bootstrap Iterative Closest Point, Hernandez-Matas et al., Saha et al., and Chen et al.'s methods. Results show that the proposed method outperforms the best performing method. On private dataset, the proposed method achieves 1-3% higher accuracy than the best-performing method for error thresholds up to 15 pixels. It significantly outperforms other methods by 4-30% for error thresholds up to 10 pixels. On the public dataset, the proposed method marginally outperforms the best reported method. It significantly outperforms GDP ICP, Hernandez-Matas et al., and Chen et al. by a margin of 10-40%.