Abstract
We introduce the RetinaRegNet model, which can achieve state-of-the-art performance across various retinal image registration tasks. RetinaRegNet does not require training on any retinal images. It begins by establishing point correspondences between two retinal images using image features derived from diffusion models. This process involves the selection of feature points from the moving image using the SIFT algorithm alongside random point sampling. For each selected feature point, a 2D correlation map is computed by assessing the similarity between the feature vector at that point and the feature vectors of all pixels in the fixed image. The pixel with the highest similarity score in the correlation map corresponds to the feature point in the moving image. To remove outliers in the estimated point correspondences, we first applied an inverse consistency constraint, followed by a transformation-based outlier detector. This method proved to outperform the widely used random sample consensus (RANSAC) outlier detector by a significant margin. To handle large deformations, we utilized a two-stage image registration framework. A homography transformation was used in the first stage and a more accurate third-order polynomial transformation was used in the second stage. The model's effectiveness was demonstrated across three retinal image datasets: color fundus images, fluorescein angiography images, and laser speckle flowgraphy images. RetinaRegNet outperformed current state-of-the-art methods in all three datasets. It was especially effective for registering image pairs with large displacement and scaling deformations. This innovation holds promise for various applications in retinal image analysis. Our code is publicly available at this https URL.
Abstract (translated)
我们提出了RetinaRegNet模型，可以实现各种视网膜图像配准任务的当前最佳性能。RetinaRegNet不需要对任何视网膜图像进行训练。它首先通过扩散模型从图像特征中建立两个视网膜图像之间的点对应关系。这个过程涉及使用SIFT算法从运动图像中选择特征点，并进行随机点采样。对于选定的每个特征点，计算该点与固定图像中所有像素的特征向量之间的相似度张量。在张量中，相似度得分最高的像素对应于运动图像中的特征点。为了消除估计点对应关系的估计方差，我们首先应用了反一致性约束，然后应用了基于变换的异常检测。这种方法在广泛使用的随机样本一致性（RANSAC）异常检测器方面显著优于该方法。为了处理大的变形，我们使用了两阶段图像配准框架。在第一阶段，使用单应性变换；在第二阶段，使用更精确的第三阶多项式变换。该模型的有效性在三个视网膜图像数据集上得到了证实：彩色 fundus 图像，荧光素造影图像和激光散射流量图。RetinaRegNet在所有三个数据集上都超越了当前的最佳方法。尤其是在对大位移和缩放变形进行图像对齐时，该创新具有很大的潜力，为视网膜图像分析各种应用。我们的代码现在公开可用，在这个链接上：https://www.xxx。
URL
https://arxiv.org/abs/2404.16017
