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Connection

Kevin Bennett to Image Processing, Computer-Assisted

This is a "connection" page, showing publications Kevin Bennett has written about Image Processing, Computer-Assisted.
  1. Charlton JR, Xu Y, Parvin N, Wu T, Gao F, Baldelomar EJ, Morozov D, Beeman SC, Derakhshan J, Bennett KM. Image analysis techniques to map pyramids, pyramid structure, glomerular distribution, and pathology in the intact human kidney from 3-D MRI. Am J Physiol Renal Physiol. 2021 09 01; 321(3):F293-F304.
    View in: PubMed
    Score: 0.723
  2. Zhang M, Wu T, Bennett KM. Small blob identification in medical images using regional features from optimum scale. IEEE Trans Biomed Eng. 2015 Apr; 62(4):1051-62.
    View in: PubMed
    Score: 0.467
  3. Pedersen M, Irrera P, Dastr? W, Z?llner FG, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR, Longo DL. Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts. Methods Mol Biol. 2021; 2216:205-227.
    View in: PubMed
    Score: 0.174
  4. Z?llner FG, Dastr? W, Irrera P, Longo DL, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR. Analysis Protocol for Dynamic Contrast Enhanced (DCE) MRI of Renal Perfusion and Filtration. Methods Mol Biol. 2021; 2216:637-653.
    View in: PubMed
    Score: 0.174
  5. Xu Y, Wu T, Gao F, Charlton JR, Bennett KM. Improved small blob detection in 3D images using jointly constrained deep learning and Hessian analysis. Sci Rep. 2020 01 15; 10(1):326.
    View in: PubMed
    Score: 0.163
  6. Baldelomar EJ, Charlton JR, deRonde KA, Bennett KM. In vivo measurements of kidney glomerular number and size in healthy and Os/+ mice using MRI. Am J Physiol Renal Physiol. 2019 10 01; 317(4):F865-F873.
    View in: PubMed
    Score: 0.157
  7. Bennett KM, Beeman SC, Baldelomar EJ, Zhang M, Wu T, Hann BD, Bertram JF, Charlton JR. Use of Cationized Ferritin Nanoparticles to Measure Renal Glomerular Microstructure with MRI. Methods Mol Biol. 2016; 1397:67-79.
    View in: PubMed
    Score: 0.123
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.
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