 Connection
Co-Authors
This is a "connection" page, showing publications co-authored by Ingrid Reiser and Robert Nishikawa.
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Connection Strength |
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2.541 |
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Reiser I, Edwards A, Nishikawa RM. Validation of a power-law noise model for simulating small-scale breast tissue. Phys Med Biol. 2013 Sep 07; 58(17):6011-27.
Score: 0.545
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Reiser I, Lee S, Nishikawa RM. On the orientation of mammographic structure. Med Phys. 2011 Oct; 38(10):5303-6.
Score: 0.479
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Reiser I, Nishikawa RM. Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. Med Phys. 2010 Apr; 37(4):1591-600.
Score: 0.431
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Reiser I, Nishikawa RM. Identification of simulated microcalcifications in white noise and mammographic backgrounds. Med Phys. 2006 Aug; 33(8):2905-11.
Score: 0.335
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Reiser I, Nishikawa RM, Giger ML, Boone JM, Lindfors KK, Yang K. Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys. 2012 Feb; 39(2):866-73.
Score: 0.123
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Engstrom E, Reiser I, Nishikawa R. Comparison of power spectra for tomosynthesis projections and reconstructed images. Med Phys. 2009 May; 36(5):1753-8.
Score: 0.101
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Reiser I, Nishikawa RM, Edwards AV, Kopans DB, Schmidt RA, Papaioannou J, Moore RH. Automated detection of microcalcification clusters for digital breast tomosynthesis using projection data only: a preliminary study. Med Phys. 2008 Apr; 35(4):1486-93.
Score: 0.094
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Reiser I, Nishikawa RM, Giger ML, Wu T, Rafferty EA, Moore R, Kopans DB. Computerized mass detection for digital breast tomosynthesis directly from the projection images. Med Phys. 2006 Feb; 33(2):482-91.
Score: 0.081
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Reiser I, Nishikawa RM, Giger ML, Wu T, Rafferty E, Moore RH, Kopans DB. 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.
Score: 0.074
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Lee J, Nishikawa RM, Reiser I, Boone JM. 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.
Score: 0.048
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Lee J, Nishikawa RM, Reiser I, Boone JM. Neutrosophic segmentation of breast lesions for dedicated breast computed tomography. J Med Imaging (Bellingham). 2018 Jan; 5(1):014505.
Score: 0.047
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Lee J, Nishikawa RM, Reiser I, Zuley ML, Boone JM. Lack of agreement between radiologists: implications for image-based model observers. J Med Imaging (Bellingham). 2017 Apr; 4(2):025502.
Score: 0.044
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Lee J, Nishikawa RM, Reiser I, Boone JM. Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT. Med Phys. 2017 May; 44(5):1846-1856.
Score: 0.044
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Lee J, Nishikawa RM, Reiser I, Boone JM, Lindfors KK. Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT. Med Phys. 2015 Sep; 42(9):5479-89.
Score: 0.039
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Lau BA, Reiser I, Nishikawa RM, Bakic PR. A statistically defined anthropomorphic software breast phantom. Med Phys. 2012 Jun; 39(6):3375-85.
Score: 0.031
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Sidky EY, Pan X, Reiser IS, Nishikawa RM, Moore RH, Kopans DB. Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. Med Phys. 2009 Nov; 36(11):4920-32.
Score: 0.026
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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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