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.
|Title of host publication||2013 IEEE International Conference on Acoustics, Speech and Signal Processing|
|Number of pages||5|
|Publication status||Published - 21 Oct 2013|
|Event||2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Vancouver, Canada|
Duration: 26 May 2013 → 31 May 2013
|Conference||2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Period||26/05/13 → 31/05/13|