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Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure

Reel, Parminder Singh; Dooley, Laurence S.; Wong, K. C. P. and Börner, Anko (2013). Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure. In: 20th International Conference on Image Processing (ICIP), 15-18 Sep 2013, Melbourne, Australia.

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Multimodal retinal images (RI) are extensively used for analysing various eye diseases and conditions such as myopia and diabetic retinopathy. The incorporation of either two or more RI modalities provides complementary structure information in the presence of non-uniform illumination and low-contrast homogeneous regions. It also presents significant challenges for retinal image registration (RIR). This paper investigates how the Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) algorithm can effectively achieve multimodal RIR. This iterative hybrid-based similarity measure combines spatial features with mutual information to provide enhanced registration without recourse to either segmentation or feature extraction. Experimental results for clinical multimodal RI datasets comprising colour fundus and scanning laser ophthalmoscope images confirm EMPCA-MI is able to consistently afford superior numerical and qualitative registration performance compared with existing RIR techniques, such as the bifurcation structures method.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 IEEE
Keywords: image registration; ophthalmological image processing; principal component analysis; mutual information; expectation-maximization algorithms
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: 37716
Depositing User: Parminder Reel
Date Deposited: 03 Jun 2013 08:12
Last Modified: 07 Dec 2018 23:05
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