title: Stochastic optimization with randomized smoothing for image registration.
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.
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, USA. Electronic address: sunwei@ieee.org.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Department of Image Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.
authors.forename: Dirk H J
authors.initials: DHJ
authors.lastname: Poot
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Ihor
authors.initials: I
authors.lastname: Smal
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China.
authors.forename: Xuan
authors.initials: X
authors.lastname: Yang
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands; Department of Image Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands.
authors.forename: Wiro J
authors.initials: WJ
authors.lastname: Niessen
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Stefan
authors.initials: S
authors.lastname: Klein
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000000981:diagnostic imaging; D005347:Fibroblasts / Q000166:cytology; D006321:Heart / Q000000981:diagnostic imaging; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051379:Mice; D015203:Reproducibility of Results; D013269:Stochastic Processes; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Image registration; Local minima; Randomized smoothing; Stochastic gradient descent