Orientation-independent Feature Matching (OIFM) for Multimodal Retinal Image Registration
Orientation-independent Feature Matching (OIFM) for Multimodal Retinal Image Registration
复制标题
用于多模态视网膜图像配准的方向无关特征匹配 (OIFM)
DOI：
10.1016/j.bspc.2020.101957
复制
发表时间：
2020-07-01
影响因子：
5.1
通讯作者：
Chen, Xin
中科院分区：
文献类型：
--
作者：
Li, Qiaoliang;Li, Shiyu;Chen, Xin
在眼科领域，多模态视网膜图像的数据整合能够极大地助力眼底图像分析，这可通过基于由特定描述子表示的关键点匹配的图像配准来实现。然而，由于传统特征描述子对多模态图像中关键点主方向的估计不一致，其提供的匹配结果可能会受到严重影响。在本文中，我们提出一种方向无关特征匹配（OIFM）方法，以更好地匹配多模态视网膜图像中的特征点。首先，利用一种新的基于圆形邻域的特征描述子来表示图像中检测到的关键点，通过循环移动描述子中的元素，可实现邻域围绕关键点的旋转。然后，将所提出的特征描述子应用于关键点匹配，在补偿方向差异后测量其距离。所提出的OIFM方法有两个显著特点。其一，该特征描述子不再依赖主方向的计算，而是通过以圆形顺序堆叠关键点邻域的特征向量，并方便地指定一个参考来构建。其二，在匹配阶段实现方向无关性，在此阶段特征点以全局方式对齐，这使得OIFM方法对多模态视网膜图像间的旋转和内容变化更具鲁棒性。对总共160对多模态视网膜图像的实验结果表明，所提出的OIFM方法在配准精度和鲁棒性方面优于传统算法。（C）2020爱思唯尔有限公司。保留所有权利。
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. (C) 2020 Elsevier Ltd. All rights reserved.