BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. ntroduction: The increased importance of automated computer techniques for anatomical brain mapping from MR images and quantitative brain image analysis methods leads to an increased need for validation and evaluation of the effect of image acquisition parameters on performance of these procedures. Validation of analysis techniques of in-vivo acquired images is complicated due to the lack of reference data (”ground truth”). Also, optimal selection of the MR imaging parameters is difficult due to the large parameter space. BrainWeb makes available to the neuroimaging community, online on WWW, a set of realistic simulated brain MR image volumes (Simulated Brain Database, SBD) that allows the above issues to be examined in a controlled, systematic way. Methods: The 3D simulated MR images are generated by varying specific imaging parameters and artifacts in an MRI simulator, which: ffl starts from a fuzzy digital phantom containing the spatial pro.

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  1. Martín, Adrián; Schiavi, Emanuele; Segura de León, Sergio: On 1-Laplacian elliptic equations modeling magnetic resonance image Rician denoising (2017)
  2. Cong, Wang; Song, Jianhua; Luan, Kuan; Liang, Hong; Wang, Lei; Ma, Xingcheng; Li, Jin: A modified brain MR image segmentation and bias field estimation model based on local and global information (2016)
  3. Ehrhardt, Matthias J.; Betcke, Marta M.: Multicontrast MRI reconstruction with structure-guided total variation (2016)
  4. Gholami, Amir; Mang, Andreas; Biros, George: An inverse problem formulation for parameter estimation of a reaction-diffusion model of low grade gliomas (2016)
  5. Chang, Liu; ChaoBang, Gao; Xi, Yu: A MRI denoising method based on 3D nonlocal means and multidimensional PCA (2015)
  6. Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao: Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy $C$-means clustering (2015)
  7. Maitra, Ranjan: On the expectation-maximization algorithm for Rice-Rayleigh mixtures with application to noise parameter estimation in magnitude MR datasets (2013)
  8. McDaniel, Joshua; Kostelich, Eric; Kuang, Yang; Nagy, John; Preul, Mark C.; Moore, Nina Z.; Matirosyan, Nikolay L.: Data assimilation in brain tumor models (2013)
  9. Nie, Fangyan; Wang, Yonglin; Pan, Meisen; Peng, Guanghan; Zhang, Pingfeng: Two-dimensional extension of variance-based thresholding for image segmentation (2013)
  10. Yu, Tsz-Ho; Woodford, Oliver J.; Cipolla, Roberto: A performance evaluation of volumetric 3D interest point detectors (2013) ioport
  11. Caldairou, Beno{^i}t; Passat, Nicolas; Habas, Piotr A.; Studholme, Colin; Rousseau, François: A non-local fuzzy segmentation method: Application to brain MRI (2011) ioport
  12. Passat, Nicolas; Naegel, Beno{^i}t; Rousseau, François; Koob, Mériam; Dietemann, Jean-Louis: Interactive segmentation based on component-trees (2011)
  13. Bardera, Anton; Feixas, Miquel; Boada, Imma; Sbert, Mateu: Image registration by compression (2010) ioport
  14. Viswanathan, Adityavikram; Gelb, Anne; Cochran, Douglas; Renaut, Rosemary: On reconstruction from non-uniform spectral data (2010)
  15. Bardera, A.; Boada, I.; Feixas, M.; Sbert, M.: Image segmentation using excess entropy (2009) ioport
  16. Bergmann, ørjan; Christiansen, Oddvar; Lie, Johan; Lundervold, Arvid: Shape-adaptive DCT for denoising of 3D scalar and tensor valued images (2009) ioport
  17. Luo, Jianhua H.; Luo, Huan; Zhu, Yuemin M.: Image reconstruction scheme based on phase correction and singularity function analysis model (2009) ioport
  18. Yang, Xu-Lei; Song, Qing; Wang, Yue; Cao, Ai-Ze; Wu, Yi-Lei: A modified deterministic annealing algorithm for robust image segmentation (2008) ioport