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Particle-guided Image Registration

1Neuro Image Research and Analysis Laboratories, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
2Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
3Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
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Pt 3
Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 3):203-10.
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Appears in Collections:
P01 DA022446/DA/NIDA NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
R41 NS059095/NS/NINDS NIH HHS/United States
U24 AA020022/AA/NIAAA NIH HHS/United States
U01 AA020023/AA/NIAAA NIH HHS/United States
U24 AA020024/AA/NIAAA NIH HHS/United States
R01 AA006059/AA/NIAAA NIH HHS/United States
U01 AA019969/AA/NIAAA NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Lee J., Lyu I., Oguz I., Styner M. Particle-guided Image Registration. Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 3):203-10. PMID: 24505762. PMCID: PMC3974564.
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We present a novel image registration method based on B-spline free-form deformation that simultaneously optimizes particle correspondence and image similarity metrics. Different from previous B-spline based registration methods optimized w.r.t. the control points, the deformation in our method is estimated from a set of dense unstructured pair of points, which we refer as corresponding particles. As intensity values are matched on the corresponding location, the registration performance is iteratively improved. Moreover, the use of corresponding particles naturally extends our method to a group-wise registration by computing a mean of particles. Motivated by a surface-based group-wise particle correspondence method, we developed a novel system that takes such particles to the image domain, while keeping the spirit of the method similar. The core algorithm both minimizes an entropy based group-wise correspondence metric as well as maximizes the space sampling of the particles. We demonstrate the results of our method in an application of rodent brain structure segmentation and show that our method provides better accuracy in two structures compared to other registration methods.

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