Enhanced retinal image registration accuracy using expectation maximisation and variable bin-sized mutual information

Abstract: While retinal images (RI) assist in the diagnosis of various eye conditions and diseases such as glaucoma and diabetic retinopathy, their innate features including low contrast homogeneous and nonuniformly illuminated regions, present a particular challenge for retinal image registration (RIR). Recently, the hybrid similarity measure, Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) has been proposed for RIR. This paper investigates incorporating various fixed and adaptive bin size selection strategies to estimate the probability distribution in the mutual information (MI) stage of EMPCA-MI, and analyses their corresponding effect upon RIR performance. Experimental results using a clinical mono-modal RI dataset confirms that  adaptive bin size selection consistently provides both lower RIR errors and superior robustness compared to the empirically determined fixed
bin sizes.

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

Poster presented at: 39th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP), 4th to 9th May 2014, Florence, Italy.