World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
5427
World Ranking
8693
National Ranking
2413

Craig K. Abbey publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Craig K. Abbey sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 258 publications — 67th percentile

67% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Craig K. Abbey D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Craig K. Abbey sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 36 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Craig K. Abbey is affiliated with the University of California, Santa Barbara in the United States. Their research is primarily situated within the field of Medicine, with a significant focus on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Artificial Intelligence, Biomedical Engineering, and Oncology.

Their scholarly contributions encompass a range of topics related to medical imaging and cancer detection, including Medical Imaging Techniques and Applications, Digital Radiography and Breast Imaging, AI in cancer detection, Advanced X-ray and CT Imaging, Global Cancer Incidence and Screening, Radiomics and Machine Learning in Medical Imaging, and Cardiac Imaging and Diagnostics.

Craig K. Abbey has published numerous papers in various scientific venues. Frequent publication venues include:

  • Journal of Medical Imaging
  • Medical Physics
  • arXiv (Cornell University)
  • Journal of Vision
  • IEEE Transactions on Medical Imaging

Some of the recent papers include:

  • Validation of synthesized normal-resolution image data generated from high-resolution acquisitions on a commercial CT scanner, 2020, Medical Physics
  • Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets, 2021, Current Biology
  • Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT, 2020, Medical Physics
  • Foveated Model Observers for Visual Search in 3D Medical Images, 2020, IEEE Transactions on Medical Imaging
  • Does scent attractiveness reveal women's ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odour attractiveness, 2022, Proceedings of the Royal Society B Biological Sciences

Craig K. Abbey frequently collaborates with several other researchers, including:

  • Miguel P. Eckstein
  • Andrew M. Hernandez
  • John M. Boone
  • Ioannis Sechopoulos
  • Michael A. Webster

Best Publications

  • Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability.

    Craig K. Abbey;Harrison H. Barrett

  • Objective assessment of image quality. III. ROC metrics, ideal observers, and likelihood-generating functions

    Harrison H. Barrett;Craig K. Abbey;Eric Clarkson

  • Visual signal detectability with two noise components: anomalous masking effects.

    Arthur E. Burgess;Xing Li;Craig K. Abbey

  • The footprints of visual attention in the Posner cueing paradigm revealed by classification images

    Miguel P. Eckstein;Steven S. Shimozaki;Craig K. Abbey

  • APPARATUS FOR GENERATING VISUAL IMAGES OF THE INTERIOR OF THE HUMAN BODY

    Close Robert A;Whiting James S;Abbey Craig K

  • Statistical texture synthesis of mammographic images with super-blob lumpy backgrounds.

    François O. Bochud;Craig K. Abbey;Miguel P. Eckstein

  • Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

    Craig K. Abbey;Miguel P. Eckstein

  • Characterizing anatomical variability in breast CT images.

    Kathrine G. Metheany;Craig K. Abbey;Nathan Packard;John M. Boone

  • Stabilized estimates of Hotelling-observer detection performance in patient-structured noise

    Harrison H. Barrett;Craig K. Abbey;Brandon D. Gallas;Miguel P. Eckstein

  • Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks.

    Miguel P. Eckstein;Jay L. Bartroff;Craig K. Abbey;James S. Whiting

  • Toward Fully Automated High-Resolution Electron Tomography

    Jennifer C. Fung;Weiping Liu;W.J. de Ruijter;Hans Chen

  • A Practical Guide to Model Observers for Visual Detection in Synthetic and Natural Noisy Images

    Miguel P. Eckstein;Craig K. Abbey;François O. Bochud

  • Linear system models for ultrasonic imaging: application to signal statistics

    R.J. Zemp;C.K. Abbey;M.F. Insana

  • Visual signal detection in structured backgrounds. III. Calculation of figures of merit for model observers in statistically nonstationary backgrounds.

    François O. Bochud;Craig K. Abbey;Miguel P. Eckstein

  • Practical issues and methodology in assessment of image quality using model observers

    Craig K. Abbey;Harrison H. Barrett;Miguel P. Eckstein

  • Association between power law coefficients of the anatomical noise power spectrum and lesion detectability in breast imaging modalities

    Lin Chen;Craig K Abbey;John M Boone

  • Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner.

    Miguel P Eckstein;Craig K Abbey;Binh T Pham;Steven S Shimozaki

  • Neural decoding of collective wisdom with multi-brain computing

    Miguel P. Eckstein;Koel Das;Binh T. Pham;Matthew F. Peterson

  • Observer signal-to-noise ratios for the ML-EM algorithm.

    Craig K. Abbey;Harrison H. Barrett;Donald W. Wilson

  • Visual signal detection in structured backgrounds. IV. Figures of merit for model performance in multiple-alternative forced-choice detection tasks with correlated responses.

    Miguel P. Eckstein;Craig K. Abbey;François O. Bochud

  • Comparison of two weighted integration models for the cueing task: linear and likelihood.

    Steven S. Shimozaki;Miguel P. Eckstein;Craig K. Abbey

Frequent Co-Authors

Miguel P. Eckstein
Miguel P. Eckstein University of California, Santa Barbara
Michael F. Insana
Michael F. Insana University of Illinois at Urbana-Champaign
Harrison H. Barrett
Harrison H. Barrett University of Arizona
Michael A. Webster
Michael A. Webster University of Nevada Reno
Ehsan Samei
Ehsan Samei Duke University
Kyle J. Myers
Kyle J. Myers Texas A&M University
Robert D. Cardiff
Robert D. Cardiff University of California, Davis
Simon R. Cherry
Simon R. Cherry University of California, Davis
John W. Sedat
John W. Sedat University of California, San Francisco
David A. Agard
David A. Agard University of California, San Francisco

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