Abstract
With availability of different retinal imaging modalities such as fundus photography and spectral domain optical coherence tomography (SD-OCT), having a robust and accurate registration scheme to enable utilization of this complementary information is beneficial. The few existing fundus-OCT registration approaches contain a vessel segmentation step, as the retinal blood vessels are the most dominant structures that are in common between the pair of images. However, errors in the vessel segmentation from either modality may cause corresponding errors in the registration. In this paper, we propose a feature-based registration method for registering fundus photographs and SD-OCT projection images that benefits from vasculature structural information without requiring blood vessel segmentation. In particular, after a preprocessing step, a set of control points (CPs) are identified by looking for the corners in the images. Next, each CP is represented by a feature vector which encodes the local structural information via computing the histograms of oriented gradients (HOG) from the neighborhood of each CP. The best matching CPs are identified by calculating the distance of their corresponding feature vectors. After removing the incorrect matches the best affine transform that registers fundus photographs to SD-OCT projection images is computed using the random sample consensus (RANSAC) method. The proposed method was tested on 44 pairs of fundus and SD-OCT projection images of glaucoma patients and the result showed that the proposed method successfully registers the multimodal images and produced a registration error of 25.34 ± 12.34 μm (0.84 ± 0.41 pixels).
Highlights
Fundus imaging and spectral domain-optical coherence tomography (SD-OCT) are two common types of imaging modalities that provide different information about the human retina
In our feature-based framework, we propose to identify the control points (CPs) from the actual images by detecting the corners in the images using the features from accelerated segment test (FAST) corner detection approach [28, 29] which, as its name suggests, is very fast and computationally efficient
We proposed a feature-based registration method for aligning optic nerve headcentered SD-OCT volumes and fundus photographs
Summary
Fundus imaging and spectral domain-optical coherence tomography (SD-OCT) are two common types of imaging modalities that provide different information about the human retina. Spectral-domain OCT, despite its recent appearance (the first SD-OCT device became commercially available less than 10 years ago [2]), has been the clinical standard of care for several eye diseases [1] This is due to providing 3D information of retinal structures such as intraretinal layers and optic nerve head that are not available via fundus imaging. Both fundus and OCT imaging techniques are vastly utilized in diagnosis and management of eye diseases such as diabetic retinopathy, glaucoma, and age-related macular degeneration (AMD). Combining complementary information from different imaging modalities could benefit physicians in their diagnosis and monitoring the ophthalmic diseases, and is advantageous for automated techniques that are utilized for processing imaging data
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