Multimodal Registration of Retinal Images

Abstract

Registration of multimodal retinal images such as fundus and Optical Coherence Tomography (OCT) images is important as the two structural imaging modalities provide complementary views of the retina. This enables a more accurate assessment of the health of the retina. However, registration is a challenging task because fundus image (2D) is obtained via optical projection whereas the OCT image (3D) is derived via optical coherence and is very noisy. Furthermore, the field of view of imaging possible in the two modalities is very different resulting in low overlap (5–20%) between the obtained images. Existing methods for this task rely on either key-point (junction/corner) detection or accurate segmentation of vessels which is difficult due to noise. We propose a registration algorithm for finding efficient landmarks under noisy conditions. The method requires neither accurate structure segmentation nor key-point detection. The Modality Independent Neighborhood Descriptor (MIND) features are used to represent landmarks to achieve insensitivity to noise, contrast. Similarity transformation is used to register images. Evaluation of the proposed method on 142 fundus-OCT pairs results in an RMSE of 2.61 pixels. The proposed method outperforms the existing algorithm in terms of robustness, accuracy, and computational efficiency.