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Connection

Dimitris Metaxas to Image Interpretation, Computer-Assisted

This is a "connection" page, showing publications Dimitris Metaxas has written about Image Interpretation, Computer-Assisted.
  1. An efficient conditional random field approach for automatic and interactive neuron segmentation. Med Image Anal. 2016 Jan; 27:31-44.
    View in: PubMed
    Score: 0.439
  2. Deformable segmentation via sparse representation and dictionary learning. Med Image Anal. 2012 Oct; 16(7):1385-96.
    View in: PubMed
    Score: 0.359
  3. Simplified labeling process for medical image segmentation. Med Image Comput Comput Assist Interv. 2012; 15(Pt 2):387-94.
    View in: PubMed
    Score: 0.344
  4. Shape prior modeling using sparse representation and online dictionary learning. Med Image Comput Comput Assist Interv. 2012; 15(Pt 3):435-42.
    View in: PubMed
    Score: 0.344
  5. Efficient MR image reconstruction for compressed MR imaging. Med Image Comput Comput Assist Interv. 2010; 13(Pt 1):135-42.
    View in: PubMed
    Score: 0.299
  6. Medical image computing and computer-assisted intervention--MICCAI2008. Preface. Med Image Comput Comput Assist Interv. 2008; 11(Pt 1):V-VII.
    View in: PubMed
    Score: 0.261
  7. Shape registration in implicit spaces using information theory and free form deformations. IEEE Trans Pattern Anal Mach Intell. 2006 Aug; 28(8):1303-18.
    View in: PubMed
    Score: 0.236
  8. A segmentation and tracking system for 4D cardiac tagged MR images. Conf Proc IEEE Eng Med Biol Soc. 2006; 2006:1541-4.
    View in: PubMed
    Score: 0.227
  9. A hybrid framework for 3D medical image segmentation. Med Image Anal. 2005 Dec; 9(6):547-65.
    View in: PubMed
    Score: 0.226
  10. Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials. Comput Med Imaging Graph. 2015 Apr; 41:80-92.
    View in: PubMed
    Score: 0.102
  11. Collaborative multi organ segmentation by integrating deformable and graphical models. Med Image Comput Comput Assist Interv. 2013; 16(Pt 2):157-64.
    View in: PubMed
    Score: 0.092
  12. Identifying regional cardiac abnormalities from myocardial strains using nontracking-based strain estimation and spatio-temporal tensor analysis. IEEE Trans Med Imaging. 2011 Dec; 30(12):2017-29.
    View in: PubMed
    Score: 0.082
  13. Measurement of subcellular texture by optical Gabor-like filtering with a digital micromirror device. Opt Lett. 2008 Oct 01; 33(19):2209-11.
    View in: PubMed
    Score: 0.069
  14. LV motion and strain computation from tMRI based on meshless deformable models. Med Image Comput Comput Assist Interv. 2008; 11(Pt 1):636-44.
    View in: PubMed
    Score: 0.065
  15. Identifying regional cardiac abnormalities from myocardial strains using spatio-temporal tensor analysis. Med Image Comput Comput Assist Interv. 2008; 11(Pt 1):789-97.
    View in: PubMed
    Score: 0.065
  16. Tag separation in cardiac tagged MRI. Med Image Comput Comput Assist Interv. 2008; 11(Pt 2):289-97.
    View in: PubMed
    Score: 0.065
  17. Adaptive metamorphs model for 3D medical image segmentation. Med Image Comput Comput Assist Interv. 2007; 10(Pt 1):302-10.
    View in: PubMed
    Score: 0.061
  18. Ultrasound myocardial elastography and registered 3D tagged MRI: quantitative strain comparison. Med Image Comput Comput Assist Interv. 2007; 10(Pt 1):800-8.
    View in: PubMed
    Score: 0.061
  19. Boosting and nonparametric based tracking of tagged MRI cardiac boundaries. Med Image Comput Comput Assist Interv. 2006; 9(Pt 1):636-44.
    View in: PubMed
    Score: 0.057
  20. Extraction and tracking of MRI tagging sheets using a 3D Gabor filter bank. Conf Proc IEEE Eng Med Biol Soc. 2006; 2006:711-4.
    View in: PubMed
    Score: 0.057
  21. Efficient learning by combining confidence-rated classifiers to incorporate unlabeled medical data. Med Image Comput Comput Assist Interv. 2005; 8(Pt 1):745-52.
    View in: PubMed
    Score: 0.053
  22. Sparsity techniques in medical imaging. Comput Med Imaging Graph. 2015 Dec; 46 Pt 1:1.
    View in: PubMed
    Score: 0.027
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.