An optimized seed point selection method assists digital image correlation for measuring deformation from OCT images of fundus diseases

Abstract

Fundus diseases are a significant cause of vision loss in older individuals worldwide. This study aimed to analyze deformations in fundus optical coherence tomography (OCT) images by utilizing and improving digital image correlation (DIC), which is expected to enhance the comprehensive assessment of these diseases. Seed points were identified at the intersections of blood vessel-induced shadows and the interfaces between retinal layers, providing accurate initial estimates for DIC. Despite the presence of severe decorrelation effects due to large deformation and low speckle quality, full-field deformation could be successfully measured. Verification tests were conducted using OCT images of a patient with retinal vein occlusion (RVO), and the method was subsequently applied to diabetic macular edema (DME) and polypoid choroidal vascular disease (PCV). DIC can obtain accurate deformation fields for fundus OCT images based on the proposed seed point selection method, offering critical imaging indicator for diagnosing and treating fundus diseases.