BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. Introduction: 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. Xia, Dong; Yuan, Ming; Zhang, Cun-Hui: Statistically optimal and computationally efficient low rank tensor completion from noisy entries (2021)
  2. Carlos Gavidia-Calderon; César Beltrán Castañon: Isula: A java framework for ant colony algorithms (2020) not zbMATH
  3. Curtin, Lee; Hawkins-Daarud, Andrea; Porter, Alyx B.; van der Zee, Kristoffer G.; Owen, Markus R.; Swanson, Kristin R.: A mechanistic investigation into ischemia-driven distal recurrence of glioblastoma (2020)
  4. Perrillat-Mercerot, Angélique; Miranville, Alain; Bourmeyster, Nicolas; Guillevin, Carole; Naudin, Mathieu; Guillevin, Rémy: What mathematical models can or cannot do in glioma description and understanding (2020)
  5. Jacobs, Joshua; Rockne, Russell C.; Hawkins-Daarud, Andrea J.; Jackson, Pamela R.; Johnston, Sandra K.; Kinahan, Paul; Swanson, Kristin R.: Improved model prediction of glioma growth utilizing tissue-specific boundary effects (2019)
  6. Subramanian, Shashank; Gholami, Amir; Biros, George: Simulation of glioblastoma growth using a 3D multispecies tumor model with mass effect (2019)
  7. Chakraborty, Shouvik; Mali, Kalyani: Application of multiobjective optimization techniques in biomedical image segmentation -- a study (2018)
  8. Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik; Chatterjee, Sumana; Wasim, Najir Abdul: A survey on the application of multi-objective optimization methods in image segmentation (2018)
  9. Rasch, Julian; Brinkmann, Eva-Maria; Burger, Martin: Joint reconstruction via coupled Bregman iterations with applications to PET-MR imaging (2018)
  10. Rasch, Julian; Kolehmainen, Ville; Nivajärvi, Riikka; Kettunen, Mikko; Gröhn, Olli; Burger, Martin; Brinkmann, Eva-Maria: Dynamic MRI reconstruction from undersampled data with an anatomical prescan (2018)
  11. Baxter, John S. H.; Rajchl, Martin; McLeod, A. Jonathan; Yuan, Jing; Peters, Terry M.: Directed acyclic graph continuous max-flow image segmentation for unconstrained label orderings (2017)
  12. Martín, Adrián; Schiavi, Emanuele; Segura de León, Sergio: On 1-Laplacian elliptic equations modeling magnetic resonance image Rician denoising (2017)
  13. 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)
  14. Ehrhardt, Matthias J.; Betcke, Marta M.: Multicontrast MRI reconstruction with structure-guided total variation (2016)
  15. Gholami, Amir; Mang, Andreas; Biros, George: An inverse problem formulation for parameter estimation of a reaction-diffusion model of low grade gliomas (2016)
  16. Xu, Yanxun; Müller, Peter; Telesca, Donatello: Bayesian inference for latent biologic structure with determinantal point processes (DPP) (2016)
  17. Zhang, Zhancheng; Luo, Xiaoqing; Chung, Fu-Lai; Wang, Shitong: A local and global classification machine with collaborative mechanism (2016)
  18. 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)
  19. Liu, Chang; Gao, ChaoBang; Yu, Xi: A MRI denoising method based on 3D nonlocal means and multidimensional PCA (2015)
  20. Maitra, Ranjan: On the expectation-maximization algorithm for Rice-Rayleigh mixtures with application to noise parameter estimation in magnitude MR datasets (2013)

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