Automatic wide field registration and mosaicking of OCTA images using vascularity information

Abstract

Optical Coherence Tomography Angiography (OCTA) constitutes a novel ophthalmological image modality that is characterized for being a non-invasive capture technique that allows a profound analysis of the vascular characteristics of the eye fundus. Given the restricted field of view of the eye fundus that offers each scan, the specialists frequently capture several complementary images that may be simultaneously analyzed to offer a complete and accurate diagnosis of the patient.In this work, we propose a fully automatic method to register complementary OCTA images and provide compositions for the same patient, generating a wide field of representation that allows a simpler and more direct analysis than the traditional tedious manual procedures. To achieve this, we based our proposal in a robust combination of representative features that are filtered by an accurate identification of the main retinal vasculature. This way, given the characteristic high irregularity in the fundus of the OCTA images, we avoid many variable areas that may interfere in the registration process, restricting the analysis to the most representative and stable structure of this image modality, the main retinal vasculature. In particular, we use Speeded-Up Robust Features (SURF) algorithm to extract representative features in the main vascular region that is extracted using a method that combines the analysis of the Hessian matrix followed by an hysteresis threshold process. Then, using a K-NN model, we perform the registration of the resulting features from the different OCTA images to be analyzed. Finally, the Random sample consensus (RANSAC) method is exploited to produce the final target mosaic. The proposed method presented satisfactory results in the validation experiments, with accurate values for the MSE index of 1.2566 and 1.6725 pixels for the registration of paired images an mosaics, respectively.