Retinal Image Registration for NIH’s ETDRS

Abstract

This paper presents a retinal image registration approach for National Institute of Health (NIH)’s Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents several major challenges for image registration. The proposed method effectively combines both area-based and feature-based methods in three steps. First, the vascular tree is extracted by using a local entropy thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Specifically, a local entropy-based peak selection and a multi-resolution searching schemes are developed to improve accuracy and efficiency of translation estimation. Third, we use two types of features (feature-based), landmark points and sampling points, for affine/quadratic model estimation. Simulation on 504 pairs of ETDRS retinal images shows the effectiveness of the proposed algorithm.