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 | 2186 | 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 | 1202 | 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 | 731 | 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 | 660 | 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 | 556 | 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 | 429 | 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 | 413 | 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 | 364 | 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 | 344 | 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 | 328 | 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 | 304 | 2016 |
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 | 301 | 1994 |
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 | 298 | 2020 |
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 | 292 | 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 | 284 | 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 | 269 | 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 | 267 | 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 | 248 | 2006 |
Deformable models in medical image analysis A Singh, D Terzopoulos, DB Goldgof IEEE Computer Society Press, 1998 | 224 | 1998 |
Fast fuzzy clustering TW Cheng, DB Goldgof, LO Hall Fuzzy sets and systems 93 (1), 49-56, 1998 | 194 | 1998 |