Dmitry Goldgof
Dmitry Goldgof
Distinguished Professor of Computer Science and Engineering, University of South Florida
Verified email at - Homepage
Cited by
Cited by
Radiomics: the process and the challenges
V Kumar, Y Gu, S Basu, A Berglund, SA Eschrich, MB Schabath, K Forster, ...
Magnetic resonance imaging 30 (9), 1234-1248, 2012
An experimental comparison of range image segmentation algorithms
A Hoover, G Jean-Baptiste, X Jiang, PJ Flynn, H Bunke, DB Goldgof, ...
IEEE transactions on pattern analysis and machine intelligence 18 (7), 673-689, 1996
Automatic tumor segmentation using knowledge-based techniques
MC Clark, LO Hall, DB Goldgof, R Velthuizen, FR Murtagh, MS Silbiger
IEEE transactions on medical imaging 17 (2), 187-201, 1998
Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol
R Kasturi, D Goldgof, P Soundararajan, V Manohar, J Garofolo, R Bowers, ...
IEEE transactions on pattern analysis and machine intelligence 31 (2), 319-336, 2008
Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels
M Shafiq‐ul‐Hassan, GG Zhang, K Latifi, G Ullah, DC Hunt, ...
Medical physics 44 (3), 1050-1062, 2017
Understanding transit scenes: A survey on human behavior-recognition algorithms
J Candamo, M Shreve, DB Goldgof, DB Sapper, R Kasturi
IEEE transactions on intelligent transportation systems 11 (1), 206-224, 2009
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
LM Fletcher-Heath, LO Hall, DB Goldgof, FR Murtagh
Artificial intelligence in medicine 21 (1-3), 43-63, 2001
Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches
M Zhou, J Scott, B Chaudhury, L Hall, D Goldgof, KW Yeom, M Iv, Y Ou, ...
American Journal of Neuroradiology 39 (2), 208-216, 2018
Reproducibility and prognosis of quantitative features extracted from CT images
Y Balagurunathan, Y Gu, H Wang, V Kumar, O Grove, S Hawkins, J Kim, ...
Translational oncology 7 (1), 72-87, 2014
Active learning to recognize multiple types of plankton.
T Luo, K Kramer, DB Goldgof, LO Hall, S Samson, A Remsen, T Hopkins, ...
Journal of Machine Learning Research 6 (4), 2005
Predicting malignant nodules from screening CT scans
S Hawkins, H Wang, Y Liu, A Garcia, O Stringfield, H Krewer, Q Li, ...
Journal of Thoracic Oncology 11 (12), 2120-2128, 2016
Finding covid-19 from chest x-rays using deep learning on a small dataset
LO Hall, R Paul, DB Goldgof, GM Goldgof
arXiv preprint arXiv:2004.02060, 2020
MRI segmentation using fuzzy clustering techniques
MC Clark, LO Hall, DB Goldgof, LP Clarke, RP Velthuizen, MS Silbiger
IEEE Engineering in Medicine and Biology Magazine 13 (5), 730-742, 1994
Fast accurate fuzzy clustering through data reduction
S Eschrich, J Ke, LO Hall, DB Goldgof
IEEE transactions on fuzzy systems 11 (2), 262-270, 2003
Test–retest reproducibility analysis of lung CT image features
Y Balagurunathan, V Kumar, Y Gu, J Kim, H Wang, Y Liu, DB Goldgof, ...
Journal of digital imaging 27, 805-823, 2014
Knowledge-based classification and tissue labeling of MR images of human brain
C Li, DB Goldgof, LO Hall
IEEE transactions on Medical Imaging 12 (4), 740-750, 1993
Macro-and micro-expression spotting in long videos using spatio-temporal strain
M Shreve, S Godavarthy, D Goldgof, S Sarkar
2011 IEEE international conference on automatic face & gesture recognition …, 2011
Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging
AA Maudsley, A Darkazanli, JR Alger, LO Hall, N Schuff, C Studholme, ...
NMR in Biomedicine 19 (4), 492-503, 2006
Deformable models in medical image analysis
A Singh, D Terzopoulos, DB Goldgof
IEEE Computer Society Press, 1998
Fast fuzzy clustering
TW Cheng, DB Goldgof, LO Hall
Fuzzy sets and systems 93 (1), 49-56, 1998
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