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Connection

Ioannis Kakadiaris to Image Interpretation, Computer-Assisted

This is a "connection" page, showing publications Ioannis Kakadiaris has written about Image Interpretation, Computer-Assisted.
  1. 3D dense local point descriptors for mouse brain gene expression images. Comput Med Imaging Graph. 2014 Jul; 38(5):326-36.
    View in: PubMed
    Score: 0.434
  2. Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph. 2014 Mar; 38(2):70-90.
    View in: PubMed
    Score: 0.417
  3. Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach. Med Image Anal. 2013 Aug; 17(6):649-70.
    View in: PubMed
    Score: 0.402
  4. Probabilistic segmentation of the lumen from intravascular ultrasound radio frequency data. Med Image Comput Comput Assist Interv. 2012; 15(Pt 2):454-61.
    View in: PubMed
    Score: 0.371
  5. An inverse scattering algorithm for the segmentation of the luminal border on intravascular ultrasound data. Med Image Comput Comput Assist Interv. 2009; 12(Pt 2):885-92.
    View in: PubMed
    Score: 0.302
  6. Denoising for 3-d photon-limited imaging data using nonseparable filterbanks. IEEE Trans Image Process. 2008 Dec; 17(12):2312-23.
    View in: PubMed
    Score: 0.300
  7. Image-based gating of intravascular ultrasound pullback sequences. IEEE Trans Inf Technol Biomed. 2008 May; 12(3):299-306.
    View in: PubMed
    Score: 0.288
  8. Learning-based segmentation framework for tissue images containing gene expression data. IEEE Trans Med Imaging. 2007 May; 26(5):728-44.
    View in: PubMed
    Score: 0.269
  9. Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Trans Pattern Anal Mach Intell. 2007 Apr; 29(4):640-9.
    View in: PubMed
    Score: 0.267
  10. One-class acoustic characterization applied to blood detection in IVUS. Med Image Comput Comput Assist Interv. 2007; 10(Pt 1):202-9.
    View in: PubMed
    Score: 0.263
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.