Registration of digital retinal images using landmark correspondence by expectation maximization

Abstract

A method for registering pairs of digital images of the retina is presented, using a small set of intrinsic control points whose matching is not known. Control point matching is then achieved by calculating similarity transformation (ST) coefficients for all possible combinations of control point pairs. The cluster of coefficients associated with the matched control point pairs is identified by calculating the Euclidean distance between each set of ST coefficients and its Rth nearest neighbour, followed by use of the Expectation–Maximization (EM) algorithm. Registration is then achieved using linear regression to optimize similarity, bilinear or second order polynomial transformations for the matching control point pairs. Results are presented of (a) the cross-modal image registration of an optical image and a fluorescein angiogram, (b) temporal registration of two images of an infant eye, and (c) mono-modal registration of a set of seven standard field optical photographs. For cross-modal registration, using a set of independent matched control points, points are mapped with an estimated accuracy of 2.9 pixels for 575×480 pixel images. Bilinear and second-order polynomial transformation models all prove to be appropriate for the final registration transform.