TY - JOUR
T1 - Intensity-based image registration by minimizing residual complexity
AU - Myronenko, Andriy
AU - Song, Xubo
N1 - Funding Information:
Manuscript received May 04, 2010; accepted June 06, 2010. Date of publication June 17, 2010; date of current version November 03, 2010. This work was supported in part by the National Institutes of Health (NIH) under Grant NEI R01EY013093 and in part by the National Science Foundation (NSF) under Grant IIS-0905095. This paper extends the work presented at the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL. Asterisk indicates corresponding author. *A. Myronenko is with the Department of Biomedical Engineering, School of Medicine, Oregon Health and Science University, Portland, OR 97201 USA (e-mail: [email protected]).
PY - 2010/11
Y1 - 2010/11
N2 - Accurate definition of the similarity measure is a key component in image registration. Most commonly used intensity-based similarity measures rely on the assumptions of independence and stationarity of the intensities from pixel to pixel. Such measures cannot capture the complex interactions among the pixel intensities, and often result in less satisfactory registration performances, especially in the presence of spatially-varying intensity distortions. We propose a novel similarity measure that accounts for intensity nonstationarities and complex spatially-varying intensity distortions in mono-modal settings. We derive the similarity measure by analytically solving for the intensity correction field and its adaptive regularization. The final measure can be interpreted as one that favors a registration with minimum compression complexity of the residual image between the two registered images. One of the key advantages of the new similarity measure is its simplicity in terms of both computational complexity and implementation. This measure produces accurate registration results on both artificial and real-world problems that we have tested, and outperforms other state-of-the-art similarity measures in these cases.
AB - Accurate definition of the similarity measure is a key component in image registration. Most commonly used intensity-based similarity measures rely on the assumptions of independence and stationarity of the intensities from pixel to pixel. Such measures cannot capture the complex interactions among the pixel intensities, and often result in less satisfactory registration performances, especially in the presence of spatially-varying intensity distortions. We propose a novel similarity measure that accounts for intensity nonstationarities and complex spatially-varying intensity distortions in mono-modal settings. We derive the similarity measure by analytically solving for the intensity correction field and its adaptive regularization. The final measure can be interpreted as one that favors a registration with minimum compression complexity of the residual image between the two registered images. One of the key advantages of the new similarity measure is its simplicity in terms of both computational complexity and implementation. This measure produces accurate registration results on both artificial and real-world problems that we have tested, and outperforms other state-of-the-art similarity measures in these cases.
KW - Bias field
KW - image registration
KW - nonstationary intensity distortion
KW - residual complexity
KW - sparseness
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U2 - 10.1109/TMI.2010.2053043
DO - 10.1109/TMI.2010.2053043
M3 - Article
C2 - 20562036
AN - SCOPUS:78149248595
SN - 0278-0062
VL - 29
SP - 1882
EP - 1891
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 11
M1 - 5487419
ER -