Retinal template matching and registration is an important challenge in teleophthalmology with low-cost imaging devices. However, the images from such devices generally have a small field of view (FOV) and image quality degradations, making matching difficult. In this paper, we develop an efficient and accurate retinal matching technique that combines dimension reduction and mutual information (MI), called RetinaMatch. The dimension reduction initializes the MI optimization as a coarse localization process, which narrows the optimization domain and avoids local optima. The effectiveness of RetinaMatch is demonstrated on the open fundus image database STARE with simulated reduced FOV and anticipated degradations, and on retinal images acquired by adapter-based optics attached to a smartphone. RetinaMatch achieves a success rate over 94 on human retinal images with the matched target registration errors below 2 pixels on average, excluding the observer variability, outperforming standard template matching solutions. In the application of measuring vessel diameter repeatedly, single pixel errors are expected. In addition, our method can be used in the process of image mosaicking with area-based registration, providing a robust approach when feature-based methods fail. To the best of our knowledge, this is the first template matching algorithm for retina images with small template images from unconstrained retinal areas. In the context of the emerging mixed reality market, we envision automated retinal image matching and registration methods as transformative for advanced teleophthalmology and long-term retinal monitoring.
Area-based approaches match the intensity differences of an image pair under a similarity measure, such as SSD (sum of squared differences) @cite_3 , CC (Cross-Correlation) @cite_37 and MI (mutual information) @cite_30 , then optimize the similarity measure by searching in the transformation space. Avoiding pixel-level feature detection, such approaches are more robust to poor quality images than feature-based approaches. However, retina images with sparse features and similar backgrounds are likely to lead the optimization into local extrema. Fig. shows an example of the area-based method with three similarity measures. The small template image is captured by the adapter-based optics which is registered onto a full fundus image. Both of the images are acquired by the same modality. SSD and normalized CC (NCC) do not have an obvious peak at the alignment position (0,0), giving no clear information on the alignment quality. Normalized MI (NMI) shows a maximum at the alignment position, while it still has local extrema which can interfere the global optimization. Besides, when the size difference between the template and full image is too large, the registration with MI can be computationally very expensive.
@cite_30 @cite_37 @cite_3
2039094769 135449821 2034432063 2508827254
Abstract When dealing with the registration of information from different image sources, the de facto similarity measure used is Mutual Information (MI). Although MI gives good performance in many image registration applications, recent works in thermal–visible registration have shown that other similarity measures can give results that are as accurate, if not more than MI. Furthermore, some of these measures also have the advantage of being calculated independently from each image to register, which allows them to be integrated more easily in energy minimization frameworks. In this article, we investigate the accuracy of similarity measures for thermal–visible image registration of human silhouettes, including MI, Sum of Squared Differences (SSD), Normalized Cross-Correlation (NCC), Histograms of Oriented Gradients (HOG), Local Self-Similarity (LSS), Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Census, Fast Retina Keypoint (FREAK), and Binary Robust Independent Elementary Feature (BRIEF). We tested the various similarity measures in dense stereo matching tasks over 25,000 windows to have statistically significant results. To do so, we created a new dataset in which one to five humans are walking in a scene in various depth planes. Results show that even if MI is a very strong performer, particularly for large regions of interest (ROI), LSS gives better accuracies when ROI are small or segmented into small fragments because of its ability to capture shape. The other tested similarity measures did not give consistently accurate results. Direct visual tracking can be impaired by changes in illumination if the right choice of similarity function and photometric model is not made. Tracking using the sum of squared differences, for instance, often needs to be coupled with a photometric model to mitigate illumination changes. More sophisticated similarities, e.g. mutual information and cross cumulative residual entropy, however, can cope with complex illumination variations at the cost of a reduction of the convergence radius, and an increase of the computational effort. In this context, the normalized cross correlation (NCC) represents an interesting alternative. The NCC is intrinsically invariant to affine illumination changes, and also presents low computational cost. This article proposes a new direct visual tracking method based on the NCC. Two techniques have been developed to improve the robustness to complex illumination variations and partial occlusions. These techniques are based on subregion clusterization, and weighting by a residue invariant to affine illumination changes. The last contribution is an efficient Newton-style optimization procedure that does not require the explicit computation of the Hessian. The proposed method is compared against the state of the art using a benchmark database with ground-truth, as well as real-world sequences. A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications. Linking two data sources is a basic building block in numerous computer vision problems. Canonical Correlation Analysis (CCA) achieves this by utilizing a linear optimizer in order to maximize the correlation between the two views. Recent work makes use of non-linear models, including deep learning techniques, that optimize the CCA loss in some feature space. In this paper, we introduce a novel, bi-directional neural network architecture for the task of matching vectors from two data sources. Our approach employs two tied neural network channels that project the two views into a common, maximally correlated space using the Euclidean loss. We show a direct link between the correlation-based loss and Euclidean loss, enabling the use of Euclidean loss for correlation maximization. To overcome common Euclidean regression optimization problems, we modify well-known techniques to our problem, including batch normalization and dropout. We show state of the art results on a number of computer vision matching tasks including MNIST image matching and sentence-image matching on the Flickr8k, Flickr30k and COCO datasets.