Randomly perturbed b-splines for nonrigid image registration

Wiro J. Niessen, W Sun, S. Klein

    Research output: Contribution to journalArticleScientificpeer-review

    16 Citations (Scopus)

    Abstract

    B-splines are commonly utilized to construct the transformation model in free-form deformation (FFD) based registration. B-splines become smoother with increasing spline order. However, a higher-order B-spline requires a larger support region involving more control points, which means higher computational cost. In general, the third-order B-spline is considered as a good compromise between spline smoothness and computational cost. A lower-order function is seldom used to construct the transformation model for registration since it is less smooth. In this research, we investigated whether lower-order B-spline functions can be utilized for more efficient registration, while preserving smoothness of the deformation by using a novel random perturbation technique. With the proposed perturbation technique, the expected value of the cost function given probability density function (PDF) of the perturbation is minimized by a stochastic gradient descent optimization. Extensive experiments on 2D synthetically deformed brain images, and real 3D lung and brain scans demonstrated that the novel randomly perturbed free-form deformation (RPFFD) approach improves the registration accuracy and transformation smoothness. Meanwhile, lower-order RPFFD methods reduce the computational cost substantially.

    Original languageEnglish
    Article number7533440
    Pages (from-to)1401-1413
    Number of pages13
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume39
    Issue number7
    DOIs
    Publication statusPublished - 1 Jul 2017

    Keywords

    • B-splines
    • free-form deformation
    • Nonrigid registration
    • perturbation
    • stochastic optimization
    • transformation

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