Robust image registration using adaptive expectation maximisation based PCA

Parminder Singh Reel, Laurence S. Dooley, K. C. P. Wong, Anko Borner

Research output: Contribution to conferencePaper

Abstract

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.
Original languageEnglish
Pages105-108
Number of pages4
DOIs
Publication statusPublished - Dec 2014
Event2014 Visual Communications and Image Processing (VCIP) - Valletta, Malta
Duration: 7 Dec 201410 Dec 2014

Conference

Conference2014 Visual Communications and Image Processing (VCIP)
Period7/12/1410/12/14

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Keywords

  • Retina
  • Image registration
  • Principal component analysis
  • Mutual information
  • Subspace constraints
  • Magnetic resonance imaging
  • Robustness

Cite this

Reel, P. S., Dooley, L. S., Wong, K. C. P., & Borner, A. (2014). Robust image registration using adaptive expectation maximisation based PCA. 105-108. Paper presented at 2014 Visual Communications and Image Processing (VCIP), . https://doi.org/10.1109/VCIP.2014.7051515