FLIRT

FLIRT (FMRIB’s Linear Image Registration Tool) is a fully automated robust and accurate tool for linear (affine) intra- and inter-modal brain image registration. FLIRT is a very robust and accurate automated linear (affine) registration tool based around a multi-start, multi-resolution global optimisation method. It can be used for inter- and intra-modal registration with 2D or 3D images. In addition, it can be run with a number of different transformation models (degrees of freedom) and it implements a general cost function weighting scheme for all cost functions.


References in zbMATH (referenced in 21 articles , 1 standard article )

Showing results 1 to 20 of 21.
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  1. Feng, Long; Bi, Xuan; Zhang, Heping: Brain regions identified as being associated with verbal reasoning through the use of imaging regression via internal variation (2021)
  2. Zheng, Cuicui; Calvin, James; Gotsman, Craig: A \textscdirect-type global optimization algorithm for image registration (2021)
  3. Zhang, Jin: Constrained linear curvature image registration model and its numerical algorithm (2020)
  4. Taig, Efrat; Ben-Shahar, Ohad: Gradient surfing: a new deterministic approach for low-dimensional global optimization (2019)
  5. Frank, Lawrence R.; Galinsky, Vitaly L.: Dynamic multiscale modes of resting state brain activity detected by entropy field decomposition (2016)
  6. Bruveris, Martins; Holm, Darryl D.: Geometry of image registration: the diffeomorphism group and momentum maps (2015)
  7. Pircalabelu, Eugen; Claeskens, Gerda; Jahfari, Sara; Waldorp, Lourens J.: A focused information criterion for graphical models in fMRI connectivity with high-dimensional data (2015)
  8. Abdi, Hervé; Williams, Lynne J.; Connolly, Andrew C.; Gobbini, M. Ida; Dunlop, Joseph P.; Haxby, James V.: Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): how to assign scans to categories without using spatial normalization (2012)
  9. Aston, John A. D.; Kirch, Claudia: Evaluating stationarity via change-point alternatives with applications to fMRI data (2012)
  10. Rasmussen, Peter M.; Hansen, Lars K.; Madsen, Kristoffer H.; Churchill, Nathan W.; Strother, Stephen C.: Model sparsity and brain pattern interpretation of classification models in neuroimaging (2012) ioport
  11. Preusse, Franziska; van der Meer, Elke; Ullwer, Dorothea; Brucks, Martin; Krueger, Frank; Wartenburger, IsabellAl.: Long-term characteristics of analogical processing in high-school students with high fluid intelligence: an fMRI study (2010) MathEduc
  12. Chalfant, Julie S.; Patrikalakis, Nicholas M.: Three-dimensional object registration using wavelet features (2009) ioport
  13. Santamaría, J.; Cordón, O.; Damas, S.; García-Torres, J. M.; Quirin, A.: Performance evaluation of memetic approaches in 3D reconstruction of forensic objects (2009) ioport
  14. Verdú-Monedero, Rafael; Larrey-Ruiz, Jorge; Morales-Sánchez, Juan; Sancho-Gómez, José Luis: Fractional regularization term for variational image registration (2009)
  15. Larrey-Ruiz, Jorge; Verdú-Monedero, Rafael; Morales-Sánchez, Juan: A Fourier domain framework for variational image registration (2008)
  16. Luan, Hongxia; Qi, Feihu; Xue, Zhong; Chen, Liya; Shen, Dinggang: Multimodality image registration by maximization of quantitative-qualitative measure of mutual information (2008)
  17. Modersitzki, Jan: FLIRT with rigidity-image registration with a local non-rigidity penalty (2008) ioport
  18. Neemuchwala, Huzefa; Hero, Alfred; Carson, Paul: Image matching using alpha-entropy measures and entropic graphs (2005)
  19. Fischer, Bernd; Modersitzki, Jan: A unified approach to fast image registration and a new curvature based registration technique. (2004)
  20. Fischer, B.; Modersitzki, J.: FLIRT: A flexible image registration toolbox (2003)

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Further publications can be found at: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/flirt/#References