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
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In the registration of temporal and stereo retinal images, the rotation angle is normally less than 5^o and the scaling factor is between 0.95 and 1.05. Due to sitting constraints in the imaging process, the x translation can be more than 100pixels, but the y translation is usually small. This paper successfully incorporates these constraints in the mutual information-based registration and exploits a constrained optimization to seek an optimal registration. The proposed approach increases the success rate of the registration algorithm significantly. The impacts of the dynamic ranges of registration parameters on the registration outcome are studied and the effects of the order of rotation, scaling, and translation are also investigated. The author details a digital image registration algorithm based on the cross correlation of triple invariant image descriptors. This algorithm is applied to ocular fundus images, and its accuracy and reliability are quantified using simulated transformations, simulated noise, and a series of actual fundus images. > This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley-Liss, Inc.