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Robust Image Registration using Adaptive Expectation Maximisation based PCA

Reel, P.S.; Dooley, L.S.; Wong, K.P. and Börner, A. (2014). Robust Image Registration using Adaptive Expectation Maximisation based PCA. In: IEEE Visual Communications and Image Processing Conference (VCIP'14), 7-10 Dec 2014, Valletta, Malta, IEEE, pp. 105–108.

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Images having either the same or different modalities can be aligned using the systematic process of image registration. Inherent image characteristics including intensity non-uniformities in magnetic resonance images and large homogeneous non-vascular regions in retinal and other generic image types however, pose a significant challenge to their registration. This paper presents an adaptive expectation maximisation for principal component analysis with mutual information (aEMPCA-MI) similarity measure for image registration. It introduces a novel iterative process to adaptively select the most significant principal components using Kaiser rule and applies 4-pixel connectivity for feature extraction together with Wichard's bin size selection in calculating the MI. Both quantitative and qualitative results on a diverse range of image datasets, conclusively demonstrate the superior image registration performance of aEMPCA-MI compared with existing MI-based similarity measures.

Item Type: Conference or Workshop Item
Copyright Holders: 2014 IEEE
ISBN: 1-4799-6139-6, 978-1-4799-6139-9
Keywords: principal component analysis
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: 41015
Depositing User: Parminder Reel
Date Deposited: 30 Sep 2014 08:10
Last Modified: 07 Dec 2018 11:02
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