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Fast EM principal component analysis image registration using neighbourhood pixel connectivity

Reel, Parminder Singh; Dooley, Laurence S.; Wong, K. C. P. and Börner, Anko (2013). Fast EM principal component analysis image registration using neighbourhood pixel connectivity. In: 15th International Conference on Computer Analysis of Images and Patterns, 27-29 Aug 2013, York.

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Image registration (IR) is the systematic process of aligning two images of the same or different modalities. The registration of mono and multimodal images i.e., magnetic resonance images, pose a particular challenge due to intensity non-uniformities (INU) and noise artefacts. Recent similarity measures including regional mutual information (RMI) and expectation maximisation for principal component analysis with MI (EMPCA-MI) have sought to address this problem. EMPCA-MI incorporates neighbourhood region information to iteratively compute principal components giving superior IR performance compared with RMI, though it is not always effective in the presence of high INU. This paper presents a modified EMPCA-MI (mEMPCA-MI) similarity measure which introduces a novel pre-processing step to exploit local spatial information
using 4-and 8-pixel neighbourhood connectivity. Experimental results using diverse image datasets, conclusively demonstrate the improved IR robustness of mEMPCA-MI when adopting second-order neighbourhood representations. Furthermore, mEMPCA-MI with 4-pixel connectivity is notably more computationally efficient than EMPCA-MI.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 Springer
Keywords: image registration; mutual information; principal component analysis; expectation maximisation 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: 37751
Depositing User: Parminder Reel
Date Deposited: 10 Jun 2013 08:53
Last Modified: 07 Dec 2018 23:05
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