Orientation-independent Feature Matching (OIFM) for Multimodal Retinal Image Registration. (July 2020) - Li, Qiaoliang; Li, Shiyu; Wu, Yajie; Guo, Wei; Qi, Suwen; Huang, Gan; Chen, Siping; Liu, Zhong; Chen, Xin
Highlights: The feature descriptor no longer relies on the calculation of main orientations. Formulated by stacking feature vectors in a circular order with assigned reference. Achieve orientation independence by aligning feature points in a global manner. Robust to rotation and content variations among multimodal retinal images. Abstract: The analysis of fundus images in ophthalmology can be greatly facilitated by data integration of multimodal retinal images, which can be achieved via image registration based on the matching of keypoints represented by certain descriptors. However, the matching results offered by conventional feature descriptors may be substantially compromised due to their inconsistent estimates of the main orientation for keypoints in multimodal images. In this paper, we propose an orientation-independent feature matching (OIFM) method for better matching of feature points in multimodal retinal images. The keypoints detected in the images are firstly represented with a new circular neighborhood-based feature descriptor, allowing for the rotation of the neighborhood around the keypoints can be achieved by circularly shifting the elements in the descriptor. Then, the proposed feature descriptor is applied for the matching of keypoints, whose distance is measured after the compensation of the orientation discrepancy. The proposed OIFM method has two distinct characteristics. First, the feature descriptor no longer relies on the calculation of main orientations, and instead it is formulated by stacking the feature vectors of the keypoint neighborhood in a circular order with a conveniently assigned reference. Second, orientation independence is achieved during the matching stage where the feature points are aligned in a global manner, leading the OIFM method more robust to rotation and content variations among multimodal retinal images. Experimental results on a total of 160 pairs of multimodal retinal images show that the proposed OIFM method outperforms the conventional algorithms in terms of registration accuracy and robustness.