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Title: Image Registration based on Neural Network and Fourier Transform
Authors: Abche, Antoine 
Yaacoub, Fadi
Maalouf, Aldo
Karam, Elie 
Affiliations: Department of Electrical Engineering 
Department of Electrical Engineering 
Keywords: Backpropagation
Biomedical MRI
Feedforward neural nets
Medical image processing
Subjects: Fourier transformations
Image registration
Issue Date: 2016
Publisher: IEEE
Part of: International Conference of the IEEE Engineering in Medicine and Biology Society, 2006
Start page: 4803
End page: 4806
Conference: International Conference of the IEEE Engineering in Medicine and Biology Society (30 Aug.-3 Sept. 2006 : New York, NY, USA) 
An image registration technique based on feed forward neural network and Fourier Transform is developed and presented. In the proposed scheme, the spectrums of the acquired images are computed, the Fourier coefficients within a selected central window of each spectrum are extracted and fed as inputs to the neural network. The feed forward neural network is implemented to estimate the transformation, defined in terms of the translation, rotation and magnification parameters, to align the corresponding images. This approach does not estimate the various registration parameters separately. They are estimated simultaneously leading to a better-optimized set of registration parameters. The approach is successful and yields better results than another Fourier based registration technique. The approach is validated on 2D images. However, it can be easily extended to 3-D application.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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