Laplacian Feature Detection and Feature Alignment for Multimodal Ophthalmic Image Registration Using Phase Correlation and Hessian Affine Feature Space.

Advances in multimodal imaging have revolutionized diagnostic and treatment monitoring in ophthalmic practice. In multimodal ophthalmic imaging, geometric deformations are inevitable and they contain inherent deformations arising from heterogeneity in the optical characteristics of imaging devices and patient related factors. The registration of ophthalmic images under such conditions is challenging. We propose a novel technique that overcomes these challenges, using Laplacian feature, Hessian affine feature space and phase correlation, to register blue autofluorescence, near-infrared reflectance and color fundus photographs of the ocular posterior pole with high accuracy. Our validation analysis - that used current feature detection and extraction techniques (speed-up robust features (SURF), a concept of wind approach (KAZE), and fast retina keypoint (FREAK)), and quantitative measures (Sorensen-Dice coefficient, Jaccard index, and Kullback-Leibler divergence scores) - showed that our approach has significant merit in registering multimodal images when compared with a mix-and-match SURF-KAZE-FREAK benchmark approach. Similarly, our evaluation analysis that used a state-of-the-art qualitative measure - the mean registration error - showed that the proposed approach is significantly better than the mix-and-match SURF-KAZE-FREAK benchmark approach, as well as a cutting edge image registration technique - Linear Stack Alignment with SIFT (scale-invariant feature transform) - in registering multimodal ophthalmic images. (C) 2020 The Author(s). Published by Elsevier B.V.