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Generalized iterative most likely oriented-point (G-IMLOP) registration

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The need to align multiple representations of anatomy is a problem frequently encountered in clinical applications. A new algorithm for feature-based registration is presented that solves this problem by aligning both position and orientation information of the shapes being registered.

Methods

The iterative most likely oriented-point (IMLOP) algorithm and its generalization (G-IMLOP) to the anisotropic noise case are described. These algorithms may be understood as probabilistic variants of the popular iterative closest point (ICP) algorithm. A probabilistic model provides the framework, wherein both position information and orientation information are simultaneously optimized. Like ICP, the proposed algorithms iterate between correspondence and registration subphases. Efficient and optimal solutions are presented for implementing each subphase of the proposed methods.

Results

Experiments based on human femur data demonstrate that the IMLOP and G-IMLOP algorithms provide a strong accuracy advantage over ICP, with G-IMLOP providing additional accuracy improvement over IMLOP for registering data characterized by anisotropic noise. Furthermore, the proposed algorithms have increased ability to robustly identify an accurate versus inaccurate registration result.

Conclusion

The IMLOP and G-IMLOP algorithms provide a cohesive framework for incorporating orientation data into the registration problem, thereby enabling improvement in accuracy as well as increased confidence in the quality of registration outcomes. For shape data having anisotropic uncertainty in position and/or orientation, the anisotropic noise model of G-IMLOP enables further gains in registration accuracy to be achieved.

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Acknowledgments

Funding for this work was provided by the National Science Foundation Graduate Research Fellowship Program and Johns Hopkins University internal funds.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA

    Seth Billings & Russell Taylor

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  1. Seth Billings
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  2. Russell Taylor
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Correspondence to Seth Billings.

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Billings, S., Taylor, R. Generalized iterative most likely oriented-point (G-IMLOP) registration. Int J CARS 10, 1213–1226 (2015). https://doi.org/10.1007/s11548-015-1221-2

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  • DOI: https://doi.org/10.1007/s11548-015-1221-2

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