title: Image registration based on neural network and Fourier transform.
abstract: 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.
authors.affiliation: Electr. Eng. Dept., Univ. of Balamand, Tripoli, Lebanon.
authors.forename: Antoine B
authors.initials: AB
authors.lastname: Abche
authors.forename: Fadi
authors.initials: F
authors.lastname: Yaacoub
authors.forename: Aldo
authors.initials: A
authors.lastname: Maalouf
authors.forename: Elie
authors.initials: E
authors.lastname: Karam
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000473:pathology*; D005583:Fourier Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism; D019047:Phantoms, Imaging; D015203:Reproducibility of Results