Robust image registration using adaptive expectation maximisation based PCA

TY - CONF

T1 - Robust image registration using adaptive expectation maximisation based PCA

AU - Reel, Parminder Singh

AU - Dooley, Laurence S.

AU - Wong, K. C. P.

AU - Borner, Anko

PY - 2014/12

Y1 - 2014/12

N2 - 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.

AB - 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.

KW - Retina

KW - Image registration

KW - Principal component analysis

KW - Mutual information

KW - Subspace constraints

KW - Magnetic resonance imaging

KW - Robustness

U2 - 10.1109/VCIP.2014.7051515

DO - 10.1109/VCIP.2014.7051515

M3 - Paper

SP - 105

EP - 108

T2 - 2014 Visual Communications and Image Processing (VCIP)

Y2 - 7 December 2014 through 10 December 2014

ER -