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Connection

Co-Authors

This is a "connection" page, showing publications co-authored by Ingrid Reiser and Robert Nishikawa.
Connection Strength

2.211
  1. Validation of a power-law noise model for simulating small-scale breast tissue. Phys Med Biol. 2013 Sep 07; 58(17):6011-27.
    View in: PubMed
    Score: 0.474
  2. On the orientation of mammographic structure. Med Phys. 2011 Oct; 38(10):5303-6.
    View in: PubMed
    Score: 0.417
  3. Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. Med Phys. 2010 Apr; 37(4):1591-600.
    View in: PubMed
    Score: 0.375
  4. Identification of simulated microcalcifications in white noise and mammographic backgrounds. Med Phys. 2006 Aug; 33(8):2905-11.
    View in: PubMed
    Score: 0.291
  5. Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys. 2012 Feb; 39(2):866-73.
    View in: PubMed
    Score: 0.107
  6. Comparison of power spectra for tomosynthesis projections and reconstructed images. Med Phys. 2009 May; 36(5):1753-8.
    View in: PubMed
    Score: 0.088
  7. Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study. Med Phys. 2008 Apr; 35(4):1486-93.
    View in: PubMed
    Score: 0.082
  8. Computerized mass detection for digital breast tomosynthesis directly from the projection images. Med Phys. 2006 Feb; 33(2):482-91.
    View in: PubMed
    Score: 0.070
  9. Computerized detection of mass lesions in digital breast tomosynthesis images using two- and three dimensional radial gradient index segmentation. Technol Cancer Res Treat. 2004 Oct; 3(5):437-41.
    View in: PubMed
    Score: 0.064
  10. Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification. Med Phys. 2018 Jun 19.
    View in: PubMed
    Score: 0.041
  11. Neutrosophic segmentation of breast lesions for dedicated breast computed tomography. J Med Imaging (Bellingham). 2018 Jan; 5(1):014505.
    View in: PubMed
    Score: 0.041
  12. Lack of agreement between radiologists: implications for image-based model observers. J Med Imaging (Bellingham). 2017 Apr; 4(2):025502.
    View in: PubMed
    Score: 0.038
  13. Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT. Med Phys. 2017 May; 44(5):1846-1856.
    View in: PubMed
    Score: 0.038
  14. Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. Med Phys. 2015 Sep; 42(9):5479-89.
    View in: PubMed
    Score: 0.034
  15. A statistically defined anthropomorphic software breast phantom. Med Phys. 2012 Jun; 39(6):3375-85.
    View in: PubMed
    Score: 0.027
  16. Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. Med Phys. 2009 Nov; 36(11):4920-32.
    View in: PubMed
    Score: 0.023
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.