Robust retinal image registration using expectation maximisation with mutual information

Abstract: Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI
together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.

Index Terms— Image registration, principal component analysis, mutual information, expectation-maximization algorithms, retinopathy.

Poster presented at: 38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), 26th to 31st May 2013, Vancouver, Canada.
doi: 10.1109/ICASSP.2013.6637824