Dmitry B. Goldgof, PhD

Where You Are:
Dmitry B. Goldgof, PhD

Office  (813) 974-4055

Education And Training
  • PhD, University of Illinois at Urbana-Champaign, 1989 - Electrical & Computer Engineering
  • MS, Renselaer Polytechnic Institute, Troy, N.Y., 1985 - Electrical, Computer & Systems Engineering


The majority of Dr. Goldgof’s work focuses on the identification of novel biomarker strategies using genomics and other emerging technologies to guide clinical decision making and the identification and implementation of molecular imaging approaches to guide clinical decision making.His team’s work is related to several directions: (a) medical image analysis for identification of image features related to cancer detection and progress as well as novel molecular imaging technologies, (b) data mining methods to discover gene features related to cancer, and (c) combination of anatomical imaging features with gene features for improved outcome prediction with recent concentration on (c). Dr. Goldgof and colleagues investigated the impact on classification accuracy of gene selection approaches on filtered-to-200-gene datasets, using four datasets with 3 filters: t-test, information gain, and reliefF. They applied Iterative Feature Perturbation (IFP) and Recursive Feature Elimination (SVM-RFE) for further gene selection. They conducted a statistical analysis of accuracy across the best 50 genes using the Friedman/Holm test, which showed that IFP and SVMRFE were significantly more accurate more often when applied to the t-test-filtered gene sets. Surprisingly, the simple t-test, applied as a filter, results in the best overall SVM accuracy and is at least as accurate as the other, more complicated filter methods.

  • Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B, Schwartz LH, Basu S, Eschrich S, Gatenby RA, Gillies RJ. Test-Retest Reproducibility Analysis of Lung CT Image Features. J Digit Imaging. 2014 Jul. Pubmedid: 24990346.
  • Balagurunathan Y, Gu Y, Wang H, Kumar V, Grove O, Hawkins S, Kim J, Goldgof DB, Hall LO, Gatenby RA, Gillies RJ. Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. Transl Oncol. 2014 Feb;7(1):72-87. Pubmedid: 24772210. Pmcid: PMC3998690.
  • Zhou M, Hall L, Goldgof D, Russo R, Balagurunathan Y, Gillies R, Gatenby R. Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol. 2014 Feb;7(1):5-13. Pubmedid: 24772202. Pmcid: PMC3998688.
  • Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, Bendtsen C, Velazquez ER, Dekker A, Aerts H, Lambin P, Li X, Tian J, Gatenby RA, Gillies RJ. Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach. Pattern Recognit. 2013 Mar;46(3):692-702. Pubmedid: 23459617.
  • Elozory DT, Kramer KA, Chaudhuri B, Bonam OP, Goldgof DB, Hall LO, Mouton PR. Automatic section thickness determination using an absolute gradient focus function. J Microsc. 2012 Dec;248(3):245-259. Pubmedid: 23078150.
  • Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ. Radiomics: the process and the challenges. Magn Reson Imaging. 2012 Nov;30(9):1234-1248. Pubmedid: 22898692. Pmcid: PMC3563280.
  • Rios Velazquez E, Aerts HJ, Gu Y, Goldgof DB, De Ruysscher D, Dekker A, Korn R, Gillies RJ, Lambin P. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen. Radiother Oncol. 2012 Nov;105(2):167-173. Pubmedid: 23157978. Pmcid: PMC3749821.
  • Kramer KA, Hall LO, Goldgof DB, Remsen A, Luo T. Fast support vector machines for continuous data. Ieee Trans Syst Man Cybern B Cybern. 2009 Aug;39(4):989-1001. Pubmedid: 19336328.
  • Hore P, Hall LO, Goldgof DB. A Scalable Framework For Cluster Ensembles. Pattern Recognit. 2009 May;42(5):676-688. Pubmedid: 20160846.
  • Hore P, Hall LO, Goldgof DB, Gu Y, Maudsley AA, Darkazanli A. A Scalable Framework For Segmenting Magnetic Resonance Images. J Signal Process Syst Signal Image Video Technol. 2009 Jan;54(1-3):183-203. Pubmedid: 20046893.
  • Maudsley AA, Darkazanli A, Alger JR, Hall LO, Schuff N, Studholme C, Yu Y, Ebel A, Frew A, Goldgof D, Gu Y, Pagare R, Rousseau F, Sivasankaran K, Soher BJ, Weber P, Young K, Zhu X. Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging. NMR Biomed. 2006 Jun;19(4):492-503. Pubmedid: 16763967. Pmcid: PMC2673915.
  • Chen L, Li L, Goldgof D, George F, Chen Z, Rao A, Cragun J, Sutphen R, Lancaster J. Improving Reliability of Response Prediction to Platinum-Based Therapy by AdaBoost and Multiple Classifiers. Conf Proc IEEE Eng Med Biol Soc. 2005;5:4822-4825. Pubmedid: 17281321.
  • Luo T, Kramer K, Goldgof D, Hall L, Samson S, Remsen A, Hopkins T. Active Learning to Recognize Multiple Types of Plankton. J Machine Learn Res. 2005;6:589-613. Pubmedid: noPMID.
  • Li L, Chen L, Goldgof D, George F, Chen Z, Rao A, Cragun J, Sutphen R, Lancaster J. Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer. Conf Proc IEEE Eng Med Biol Soc. 2005;5:4818-4821. Pubmedid: 17281320.
  • Qiu Y, Li L, Goldgof D, Sarkar S, Sorin A, Clark R. Three dimensional finite element model for lesion correspondence in breast imaging. Proc Spie Med Imag. 2004;5370:1372-1379. Pubmedid: noPMID.
  • Luo T, Kramer K, Goldgof D, Hall L, Samson S, Remsen A, Hopkins T. Recognizing plankton images from the shadow image particle profiling evaluation recorder. Ieeetrans Syst Man Cybern B Cybern. 2004;34(4):1753-1762. Pubmedid: 15462442.
  • Powell M, Sarkar S, Goldgof D, Ivanov K. A methodology for extracting objective color from images. Ieee Trans Syst Man Cybern B Cybern. 2004;34(5):1964-1978. Pubmedid: 15503493.
  • Fink E, Kokku P, Nikiforou S, Hall L, Goldgof D, Krischer J. Selection of patients for clinical trials: an interactive web-based system. Artif Intell Med. 2004;31(3):241-254. Pubmedid: 15302090.
  • Zhang Y, Goldgof D, Sarkar S, Tsap L. A modeling approach for burn scar assessment using natural features and elastic property. Ieee Trans Med Imaging. 2004;23(10):1325-1329. Pubmedid: 15493699.
  • Eschrich S, Ke J, Hall LO, Goldgof DB. Fast Accurate Fuzzy Clustering through Data Reduction. Ieee Trans Fuzzy Systems. 2003;11(2):262-270. Pubmedid: noPMID.
  • Zhang M, Hall L, Goldgof D. A genetic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithm. Ieee Transactions On Systems, Man And Cybernetics. 2002;32(5):571-582. Pubmedid: noPMID.
  • Fletcher-Heath L, Hall L, Goldgof D, Reed F. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med. 2001;21(1-3):43-63. Pubmedid: 11154873.
  • Shin M, Goldgof D, Bowyer K, Nikiforou S. Comparison of edge detection algorithms using a structure from motion task. Ieee Trans Syst Man Cybern B Cybern. 2001;31(4):589-601. Pubmedid: 18244823.
  • Tsap L, Goldgof D, Sarkar S. Nonrigid motion analysis based on dynamic refinement of finite element models. Ieee: Trans Patt Anal Mach Intel. 2000;22(5):526-543. Pubmedid: noPMID.
  • Powers P, Sarkar S, Goldgof D, Cruse C, Tsap L. Scar assessment: current problems and future solutions. J Burn Care Rehabil. 1999;20(1):54-60. Pubmedid: 9934638.
  • Tsap L, Goldgof D, Sarkar S, Powers P. A vision-based technique for objective assessment of burn scars. Ieee T Med Imaging. 1998 Aug;17(4):620-633. Pubmedid: 9845317.
  • Hoover A, Goldgof D, Bowyer K. Dynamic-scale model construction from range imagery. Ieee: Trans Patt Anal Mach Intel. 1998;29(12):1352-1357. Pubmedid: noPMID.
  • Clark M, Hall L, Goldgof D, Velthuizen R, Silbiger M, Murtagh F. Automatic tumor segmentation using knowledge-based techniques. Ieee Trans Med Imag. 1998;17(2):187-201. Pubmedid: 9688151.
  • Hoover A, Goldgof D, Bowyer K. The space envelope representation for 3D scenes. Comput Vis Im Understanding. 1998;69(3):310-329. Pubmedid: noPMID.
  • Tsap L, Goldgof D, Sarkar S. Efficient nonlinear finite element modeling of nonrigid objects via optimization of mesh models. Comput Vis Im Understanding. 1998;69(3):330-350. Pubmedid: noPMID.
  • Clarke L, Velthuizen R, Clark M, Gaviria G, Hall L, Goldgof D, Murtagh R, Phuphanich S, Brem S. MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation. Magn Reson Imaging. 1998;16(3):271-279. Pubmedid: 9621968.
  • Cheng T, Goldgof D, Hall L. Fast fuzzy clustering. Int J Fuzzy Sets Systems. 1998;93(1):49-56. Pubmedid: noPMID.
  • Tsap L, Goldgof D, Sarkar S. Efficient nonlinear finite element modeling of nonrigid objects via optimization of mesh models. Computer Vision And Image Understanding. 1998;69(3):330-350. Pubmedid: noPMID.
  • Bezdek J, Hall L, Clark M, Goldgof D, Clarke L. Medical image analysis with fuzzy models. Stat Meth Med Res. 1997;6:191-214. Pubmedid: 9339497.
  • Hoover A, Jean-Baptiste J, Jiang X, Flynn P, Bunke H, Goldgof D, Bowyer K, Eggert D, Fitzgibbon A, Fisher R. An experimental comparison of range image segmentation algorithms. Ieee: Trans Patt Anal Mach Intel. 1996;18(7):673-689.
  • Stark L, Bowyer K, Hoover A, Goldgof D. Recognizing object function through reasoning about partial shape descriptions and dynamic physical properties. Proceedings Ieee. 1996;84(11):1640-1656.
  • Kumar S, Han S, Goldgof D, Bowyer K. On recovering hyperquadrics from range data. Ieee: Trans Patt Anal Mach Intel. 1995;17(11):1079-1083. Pubmedid: noPMID.
  • Hoover A, Goldgof D, Bowyer K. Extracting a valid boundary representation from a segmented range image. Ieee: Trans Patt Anal Mach Intel. 1995;17(9):920-924. Pubmedid: noPMID.
  • Shin M, Goldgof D, Bowyer K. An objective comparison methodology of edge detection algorithms using a structure from motion task. In: Bowyer K, Phillips P, eds. Empirical Evaluation Techniques in Computer Vision. Los Alamitos, CA: IEEE Computer Society Press; 1998;235-254.
  • Clark M, Hall L, Goldgof D, Silbiger M. Using fuzzy information in knowledge guided segmentation of brain tumors. In: Cabonell J, Siekman J, eds. Fuzzy logic in AI: towards intelligent systems. Springer Verlag; 1997;167-181.
  • Clark M, Hall L, Goldgof D, Velthuizen R, Murtagh F, Silbiger M. Unsupervised brain tumor segmentation using knowledge based and fuzzy techniques. In: Theodorescu H, Kandel A, Jain L, et al, eds. Fuzzy and Neuro-Fuzzy Systems in Medicine. Boca Raton, FL: CRC Press; 1999;137-169.
  • Bezdek J, Hall L, Clark M, Goldgof D, Clarke L. Segmenting medica images with fuzzy models: an update. In: Dubois D, Prade H, Yager R, et al, eds. Fuzzy information engineering. New York, NY: Wiley; 1997;69-92.
  • Hoover A, Goldgof D, Stark L, Bowyer K. Function-based analysis using partial shape, Chapter VIII. In: Stark L, Bowyer K, eds. Generic recognition using form and function. World Scientific; 1996;67-79.
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