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 conferencePaperpeer-review


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


Conference2014 Visual Communications and Image Processing (VCIP)


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


Dive into the research topics of 'Robust image registration using adaptive expectation maximisation based PCA'. Together they form a unique fingerprint.

Cite this