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.
Original language | English |
---|---|
Title of host publication | 2013 IEEE International Conference on Acoustics, Speech and Signal Processing |
Publisher | IEEE |
Pages | 1118-1122 |
Number of pages | 5 |
ISBN (Electronic) | 9781479903566 |
DOIs | |
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
Conference | 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
---|---|
Country/Territory | Canada |
City | Vancouver |
Period | 26/05/13 → 31/05/13 |