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Robust retinal image registration using expectation maximisation with mutual information

Reel, Parminder; Dooley, Laurence and Wong, Patrick (2013). Robust retinal image registration using expectation maximisation with mutual information. In: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 26-31 May 2013, Vancouver, Canada, pp. 1118–1122.

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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 non-uniform 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.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 IEEE
Keywords: image registration; principal component analysis; mutual information; expectation-maximization algorithms; retinopathy
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
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Item ID: 36895
Depositing User: Parminder Reel
Date Deposited: 15 Mar 2013 10:02
Last Modified: 04 Jun 2020 09:29
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