Print Email Facebook Twitter Stochastic optimization with randomized smoothing for image registration Title Stochastic optimization with randomized smoothing for image registration Author Sun, Wei (Erasmus MC; University of Southern California) Poot, D.H.J. (TU Delft ImPhys/Quantitative Imaging; Erasmus MC) Smal, Ihor (Erasmus MC) Yang, Xuan (Shenzhen University) Niessen, W.J. (TU Delft ImPhys/Quantitative Imaging; Erasmus MC) Klein, S. (Erasmus MC) Date 2017 Abstract Image registration is typically formulated as an optimization process, which aims to find the optimal transformation parameters of a given transformation model by minimizing a cost function. Local minima may exist in the optimization landscape, which could hamper the optimization process. To eliminate local minima, smoothing the cost function would be desirable. In this paper, we investigate the use of a randomized smoothing (RS) technique for stochastic gradient descent (SGD) optimization, to effectively smooth the cost function. In this approach, Gaussian noise is added to the transformation parameters prior to computing the cost function gradient in each iteration of the SGD optimizer. The approach is suitable for both rigid and nonrigid registrations. Experiments on synthetic images, cell images, public CT lung data, and public MR brain data demonstrate the effectiveness of the novel RS technique in terms of registration accuracy and robustness. Subject Image registrationLocal minimaRandomized smoothingStochastic gradient descent To reference this document use: http://resolver.tudelft.nl/uuid:54341eb8-4282-4d17-aa74-ff41bbe65ff2 DOI https://doi.org/10.1016/j.media.2016.07.003 Embargo date 2018-07-13 ISSN 1361-8415 Source Medical Image Analysis, 35, 146-158 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2017 Wei Sun, D.H.J. Poot, Ihor Smal, Xuan Yang, W.J. Niessen, S. Klein Files PDF Sun17_preprint_Stochastic ... ration.pdf 742.38 KB Close viewer /islandora/object/uuid:54341eb8-4282-4d17-aa74-ff41bbe65ff2/datastream/OBJ/view