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Connection

Samuel G. Armato to Radiographic Image Enhancement

This is a "connection" page, showing publications Samuel G. Armato has written about Radiographic Image Enhancement.
  1. Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans. J Digit Imaging. 2015 Dec; 28(6):755-60.
    View in: PubMed
    Score: 0.454
  2. Lung texture in serial thoracic CT scans: assessment of change introduced by image registration. Med Phys. 2012 Aug; 39(8):4679-90.
    View in: PubMed
    Score: 0.360
  3. Temporal subtraction of dual-energy chest radiographs. Med Phys. 2006 Jun; 33(6):1911-9.
    View in: PubMed
    Score: 0.235
  4. Temporal subtraction in chest radiography: automated assessment of registration accuracy. Med Phys. 2006 May; 33(5):1239-49.
    View in: PubMed
    Score: 0.234
  5. Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys. 2003 Jun; 30(6):1188-97.
    View in: PubMed
    Score: 0.191
  6. Automated detection of lung nodules in CT scans: effect of image reconstruction algorithm. Med Phys. 2003 Mar; 30(3):461-72.
    View in: PubMed
    Score: 0.188
  7. Automated registration of frontal and lateral radionuclide lung scans with digital chest radiographs. Acad Radiol. 2000 Jul; 7(7):530-9.
    View in: PubMed
    Score: 0.156
  8. Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility. J Digit Imaging. 1999 Feb; 12(1):34-42.
    View in: PubMed
    Score: 0.141
  9. Computerized delineation and analysis of costophrenic angles in digital chest radiographs. Acad Radiol. 1998 May; 5(5):329-35.
    View in: PubMed
    Score: 0.134
  10. Automated lung segmentation in digitized posteroanterior chest radiographs. Acad Radiol. 1998 Apr; 5(4):245-55.
    View in: PubMed
    Score: 0.133
  11. Automated registration of ventilation-perfusion images with digital chest radiographs. Acad Radiol. 1997 Mar; 4(3):183-92.
    View in: PubMed
    Score: 0.124
  12. Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules. J Am Coll Radiol. 2015 Apr; 12(4):390-5.
    View in: PubMed
    Score: 0.108
  13. Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs. Acad Radiol. 2015 Apr; 22(4):475-80.
    View in: PubMed
    Score: 0.107
  14. Improved detection of focal pneumonia by chest radiography with bone suppression imaging. Eur Radiol. 2012 Dec; 22(12):2729-35.
    View in: PubMed
    Score: 0.090
  15. Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med Phys. 2008 Feb; 35(2):694-703.
    View in: PubMed
    Score: 0.066
  16. The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. Acad Radiol. 2007 Nov; 14(11):1409-21.
    View in: PubMed
    Score: 0.065
  17. Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques. Med Phys. 2004 May; 31(5):1105-15.
    View in: PubMed
    Score: 0.051
  18. Digital chest radiography: effect of temporal subtraction images on detection accuracy. Radiology. 1997 Feb; 202(2):447-52.
    View in: PubMed
    Score: 0.031
  19. CT-based pulmonary artery measurements for the assessment of pulmonary hypertension. Acad Radiol. 2014 Apr; 21(4):523-30.
    View in: PubMed
    Score: 0.025
  20. Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. Med Phys. 2013 Jun; 40(6):061906.
    View in: PubMed
    Score: 0.024
  21. Vessel tree reconstruction in thoracic CT scans with application to nodule detection. IEEE Trans Med Imaging. 2005 Apr; 24(4):486-99.
    View in: PubMed
    Score: 0.014
  22. Automated matching of temporally sequential CT sections. Med Phys. 2004 Dec; 31(12):3417-24.
    View in: PubMed
    Score: 0.013
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.