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Connection

Sokratis Makrogiannis to Algorithms

This is a "connection" page, showing publications Sokratis Makrogiannis has written about Algorithms.
Connection Strength

1.430
  1. Makrogiannis S. Bone texture characterization for osteoporosis diagnosis using digital radiography. Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug; 2016:1034-1037.
    View in: PubMed
    Score: 0.446
  2. Makrogiannis S, Serai S, Fishbein KW, Schreiber C, Ferrucci L, Spencer RG. Automated quantification of muscle and fat in the thigh from water-, fat-, and nonsuppressed MR images. J Magn Reson Imaging. 2012 May; 35(5):1152-61.
    View in: PubMed
    Score: 0.324
  3. Makrogiannis S, Economou G, Fotopoulos S. A region dissimilarity relation that combines feature-space and spatial information for color image segmentation. IEEE Trans Syst Man Cybern B Cybern. 2005 Feb; 35(1):44-53.
    View in: PubMed
    Score: 0.201
  4. Boukari F, Makrogiannis S. Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling. IEEE/ACM Trans Comput Biol Bioinform. 2020 May-Jun; 17(3):959-971.
    View in: PubMed
    Score: 0.130
  5. Boukari F, Makrogiannis S. Joint level-set and spatio-temporal motion detection for cell segmentation. BMC Med Genomics. 2016 08 10; 9 Suppl 2:49.
    View in: PubMed
    Score: 0.112
  6. Makrogiannis S, Caturegli G, Davatzikos C, Ferrucci L. Computer-aided assessment of regional abdominal fat with food residue removal in CT. Acad Radiol. 2013 Nov; 20(11):1413-21.
    View in: PubMed
    Score: 0.092
  7. Makrogiannis S, Bhotika R, Miller JV, Skinner J, Vass M. Nonparametric intensity priors for level set segmentation of low contrast structures. Med Image Comput Comput Assist Interv. 2009; 12(Pt 1):239-46.
    View in: PubMed
    Score: 0.066
  8. Makrogiannis S, Verma R, Davatzikos C. Anatomical equivalence class: a morphological analysis framework using a lossless shape descriptor. IEEE Trans Med Imaging. 2007 Apr; 26(4):619-31.
    View in: PubMed
    Score: 0.058
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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