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  1. Gregory Slabaugh
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Contact Information

Contact

Visit Gregory Slabaugh

A304D, College Building

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Postal Address

City, University of London
Northampton Square
London
EC1V 0HB
United Kingdom

About

Background

Dr Gregory G. Slabaugh is a Senior Lecturer (Associate Professor) in the Department of Computer Science at City University London. He has a broad background in computer vision and medical image analysis. He earned a PhD (highest honours) in Electrical Engineering from Georgia Institute of Technology in Atlanta, USA. Dr Slabaugh has roughly a decade's experience working in industry, holding research positions at Medicsight, Siemens, and Hewlett-Packard. He is a Senior Member of IEEE and served as an Associate Editor of IEEE Signal Processing Magazine (2007 - 2012).

At City he is the founder of the Computer Vision Group.

Qualifications

PhD Electrical Engineering, Georgia Tech, 2002
MSc Electrical Engineering, Georgia Tech, 1998
BSc Engineering Physics, University of Michigan, 1994

Employment

2012 - to date City University London, Senior Lecturer
2008 - 2011 Medicsight PLC, Head of Research and Development
2003 - 2008 Siemens Corporate Research, Project Manager & Research Scientist
1999 - 2000 Hewlett-Packard Laboratories, Research Intern
1994 - 1996 Friendly Software, Lead Software Developer

Research interests

Computer vision
Medical Image Analysis
Computer Graphics

Research

At City, Dr Slabaugh leads a team of researchers in computer vision, with applications to medical imaging and computer games. Current research activities are focussed on computer-aided detection of cervical spine injuries, automatic grading of brain tumours from MRI, and 3D modeling of articulated anatomy from images. He has co-authored over 75 peer-reviewed papers and has been granted 32 patents for his work in image analysis and shape modeling; a full listing is available here. His research has been funded by the European Union, the EPSRC, and the Technology Strategy Board.

His longer-term research focuses on clinically-guided applications of medical image analysis. As the Head of Research and Development at Medicsight, he directed a research team in development of computer-aided detection (CAD) of pre-cancerous lesions in the colon and lung, relying on advanced techniques for segmentation, registration, and machine learning. This research was commercialised as software integrated into leading workstations used by radiologists, and he made key contributions to achieve regulatory approvals in numerous territories, including the USA (FDA) and and EU (CE marking). His earlier work at Siemens Corporate Research included novel techniques for ultrasound image enhancement, registration, and segmentation, as well as a 3D shape modeling application for hearing aid design.

Research Students

Name
Adriana Danilakova
Attendance
Oct 2016 – present, part-time
Thesis Title
An Ontology-based Framework for image Understanding based on Visual Information Gathered via Deep Learning
Role
1st Supervisor
Name
Socrates Katsoulakos
Attendance
Feb 2016 – present, part-time
Thesis Title
Realtime Spatio-Temporal Level of Detail Techniques
Role
1st Supervisor
Name
Atif Riaz
Attendance
Oct 2015 – present, full-time
Thesis Title
Machine Learning for Functional Connectivity Analysis of Neurological Disorders Using Magnetic Resonance Imaging
Role
1st Supervisor
Name
Nathan Olliverre
Attendance
Oct 2015 – present, full-time
Thesis Title
A Clinical System for the Assistance in Detection and Classification of Brain Neoplasms in MRI
Role
1st Supervisor
Name
Moazzam Jawaid
Attendance
Oct 2014 – present, full-time
Thesis Title
Segmentation of soft atherosclerotic plaques using active contour models
Role
1st Supervisor
Name
S M Masudur Rahman Al-Arif
Attendance
Oct 2014 – present, full-time
Thesis Title
Identification of Spinal Deformity in X-Ray Images
Role
1st Supervisor
Name
Rilwan Basru
Attendance
Oct 2013 – present, part-time
Thesis Title
Stereovision Gesture Recognition from Egocentric Video
Role
1st Supervisor
Name
Anfisa Lazareva
Attendance
Apr 2013 – present, full-time
Thesis Title
An Automated Image Processing System for the Detection of Photoreceptor cells in Adaptive Optics retinal images
Role
1st Supervisor
Name
Muhammad Asad
Attendance
Feb 2013 – Jun 2017, full-time
Thesis Title
Hand Pose and Orientation estimation for Egocentric Devices
Role
1st Supervisor

Publications

Chapters (20)

  1. Hampshire, T., Roth, H., Boone, D., Slabaugh, G.G., Halligan, S. and Hawkes, D.J. (2012). Prone to Supine CT Colonography Registration Using a Landmark and Intensity Composite Method. In Yoshida, H., Hawkes, D.J. and Vannier, M.W. (Eds.), Abdominal Imaging. Computational and Clinical Applications - 4th International Workshop, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012. Proceedings (pp. 1–9). Springer ISBN 978-3-642-33611-9.
  2. Slabaugh, G.G. and Ye, X. (2011). Concavity analysis for reduction of ileocecal valve false positives in CTC. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. (pp. 173–176). ISBN 978-1-4244-4127-3.
  3. Boyes, R., Slabaugh, G.G. and Beddoe, G. (2009). Fast pseudo-enhancement correction in CT colonography using linear shift-invariant filters. 16th IEEE International Conference on Image Processing (ICIP), 2009. (pp. 2509–2512). IEEE. ISBN 978-1-4244-5653-6.
  4. Wang, Z., Slabaugh, G.G., Zhou, M. and Fang, T. (2008). Automatic tracing of blood flow velocity in pulsed Doppler images. IEEE International Conference on Automation Science and Engineering, 2008. CASE 2008. (pp. 218–222). IEEE. ISBN 978-1-4244-2022-3.
  5. Wang, Z., Slabaugh, G.G., Unal, G. and Fang, T. (2007). Registration of Ultrasound Images Using an Information-Theoretic Feature Detector. 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. (pp. 736–739). IEEE. ISBN 1-4244-0672-2.
  6. Wang, Z., Slabaugh, G.G., Unal, G. and Zhou, M. (2007). An information-theoretic detector based scheme for registration of speckled medical images. IEEE International Conference on Systems, Man and Cybernetics, 2007. ISIC. (pp. 1026–1030). IEEE. ISBN 978-1-4244-0990-7.
  7. Slabaugh, G.G., Dinh, Q. and Unal, G. (2007). A Variational Approach to the Evolution of Radial Basis Functions for Image Segmentation. IEEE Conference on Computer Vision and Pattern Recognition, 2007 (CVPR '07) (pp. 1–8). IEEE. ISBN 1-4244-1179-3.
  8. Abufadel, A., Slabaugh, G.G., Unal, G.B., Zhang, L. and Odry, B. (2006). Interacting Active Rectangles for Estimation of Intervertebral Disk Orientation. 18th International Conference on Pattern Recognition (ICPR 2006), 20-24 August 2006, Hong Kong, China (pp. 1013–1016). IEEE Computer Society ISBN 0-7695-2521-0.
  9. Peny, B., Unal, G.B., Slabaugh, G.G., Fang, T. and Alvino, C.V. (2006). Efficient and Robust Segmentations Based on Eikonal and Diffusion PDEs. In Zheng, N., Jiang, X. and Lan, X. (Eds.), Advances in Machine Vision, Image Processing, and Pattern Analysis, International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006, Xi'an, China, August 26-27, 2006, Proceedings (pp. 339–348). Springer ISBN 3-540-37597-X.
  10. Slabaugh, G., Unal, G. and Chang, T.C. (2006). Information-theoretic feature detection in ultrasound images. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006 (EMBS '06) (pp. 2638–2642). ISBN 1-4244-0032-5.
  11. Doan, H., Slabaugh, G.G., Unal, G. and Fang, T. (2006). Semi-Automatic 3-D Segmentation of Anatomical Structures of Brain MRI Volumes using Graph Cuts. 2006 IEEE International Conference on Image Processing (pp. 1913–1916). Atlanta, USA: IEEE. ISBN 1-4244-0480-0.
  12. Slabaugh, G.G., Mihalef, V. and Unal, G.B. (2005). A Contour-Based Approach to 3D Text Labeling on Triangulated Surfaces. Fifth International Conference on 3D Digital Imaging and Modeling (3DIM 2005), 13-16 June 2005, Ottawa, Ontario, Canada (pp. 416–423). IEEE Computer Society ISBN 0-7695-2327-7.
  13. Unal, G.B. and Slabaugh, G.G. (2005). Coupled PDEs for Non-Rigid Registration and Segmentation. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20-26 June 2005, San Diego, CA, USA (pp. 168–175). IEEE Computer Society ISBN 0-7695-2372-2.
  14. Slabaugh, G.G., Schafer, R.W. and Hans, M.C. (2002). Image-Based Photo Hulls. 1st International Symposium on 3D Data Processing Visualization and Transmission (3DPVT 2002), 19-21 June 2002, Padova, Italy (pp. 704–708). IEEE Computer Society ISBN 0-7695-1521-5.
  15. Yezzi, A.J., Slabaugh, G.G., Broadhurst, A., Cipolla, R. and Schafer, R.W. (2002). A Surface Evolution Approach o Probabilistic Space Carving. 1st International Symposium on 3D Data Processing Visualization and Transmission (3DPVT 2002), 19-21 June 2002, Padova, Italy (pp. 618–621). IEEE Computer Society ISBN 0-7695-1521-5.
  16. Slabaugh, G.G., Schafer, R.W. and Hans, M.C. (2002). Multi-resolution space carving using level set methods. 2002 International Conference on Image Processing. (2002, Proceedings) (pp. 545–548). IEEE. ISBN 0-7803-7622-6.
  17. Slabaugh, G.G., Culbertson, W.B., Malzbender, T. and Schafer, R.W. (2001). A Survey of Methods for Volumetric Scene Reconstruction from Photographs. In Mueller, K. and Kaufman, A.E. (Eds.), Proceedings of the Joint IEEE TCVG and Eurographics Workshop on Volume Graphics in Stony Brook, New York, USA, June 21-22, 2001 (pp. 81–101). Eurographics Association ISBN 3-211-83737-X.
  18. Dinh, H., Turk, G. and Slabaugh, G.G. (2001). Reconstructing Surfaces Using Anisotropic Basis Functions. Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001. Proceedings. (pp. 606–613). IEEE. ISBN 0-7695-1143-0.
  19. Slabaugh, G.G., Malzbender, T. and Culbertson, W.B. (2000). Volumetric Warping for Voxel Coloring on an Infinite Domain. In Pollefeys, M., Gool, L.J.V., Zisserman, A. and Fitzgibbon, A.W. (Eds.), 3D Structure from Images - SMILE 2000, Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments Dublin, Ireland, July 12, 2000, Revised Papers (pp. 109–123). Springer ISBN 3-540-41845-8.
  20. Culbertson, W.B., Malzbender, T. and Slabaugh, G.G. (1999). Generalized Voxel Coloring. In Triggs, B., Zisserman, A. and Szeliski, R. (Eds.), Vision Algorithms: Theory and Practice, International Workshop on Vision Algorithms, held during ICCV '99, Corfu, Greece, September 21-22, 1999, Proceedings (pp. 100–115). Springer ISBN 3-540-67973-1.

Conference Papers and Proceedings (61)

  1. Riaz, A., Asad, M., Al-Arid, S.M.M.R., Alonso, E., Dima, D., Corr, P. and Slabaugh, G. (2017). FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI. 1st International Workshop on Connectomics in NeuroImaging (CNI) 14 September, Quebec City, QC, Canada.
  2. Slabaugh, G.G., Knapp, K. and Al-Arif, S.M. (2017). Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network. British Machine Vision Conference 5-7 September, London, UK.
  3. Yang, G., Zhuang, X., Khan, H., Haldar, S., Nyktari, E., Ye, X., Slabaugh, G.G., Wong, T., Mohiaddin, R., Keegan, J. and Firman, D. (2017). Segmenting Atrial Fibrosis from late Gadolinium-Enhanced Cardiac MRI by Deep-Learned Features with Stacked Sparse Auto-Encoders. Medical Image Understanding and Analysis 11 July, Edinburgh.
  4. Jawaid, M.M., Rajani, R., Liatsis, P., Reyes-Aldasoro, C.C. and Slabaugh, G. (2017). Improved CTA coronary segmentation with a volume-specific intensity threshold. .
  5. Solís-Lemus, J.A., Stramer, B., Slabaugh, G. and Reyes-Aldasoro, C.C. (2017). Segmentation of overlapping macrophages using Anglegram analysis. .
  6. Al-Arif, S.M.M.R., Gundry, M., Knapp, K. and Slabaugh, G.G. (2016). Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae. Computational Methods and Clinical Applications for Spine Imaging 17 October, Athens, Greece.
  7. Al-Arif, S.M.M.R., Gundry, M., Slabaugh, G.G. and Knapp, K. (2016). Global Localization and Orientation of the Cervical Spine in X-ray Imaging. Computational Methods and Clinical Applications for Spine Imaging 17 October, Athens, Greece.
  8. Asad, M. and Slabaugh, G.G. (2016). Learning Marginalization through Regression for Hand Orientation Inference. CVPR 2016 26 Jun 2016 – 1 Jul 2016, Las Vegas, USA.
  9. Basaru, R.R., Slabaugh, G.G., Child, C. and Alonso, E. (2016). HandyDepth: Example-based stereoscopic hand depth estimation using Eigen Leaf Node Features. .
  10. Asad, M., Yang, G., Slabaugh, G. and IEEE, (2016). Supervised Partial Volume Effect Unmixing for Brain Tumor Characterization using Multi-voxel MR Spectroscopic Imaging. .
  11. Riaz, A., Alonso, E. and Slabaugh, G. (2016). Phenotypic integrated framework for classification of ADHD using fMRI. .
  12. Yang, G., Ye, X., Slabaugh, G.G., Keegan, J., Mohiaddin, R. and Firman, D. (2016). Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI. International Conference on Image and Signal Processing .
  13. Sarkar, S., Weyde, T., Garcez, A.D.A., Slabaugh, G., Dragicevic, S. and Percy, C. (2016). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. .
  14. Percy, C., D'Avila Garcez, A.S., Dragicevic, S., França, M.V.M., Slabaugh, G. and Weyde, T. (2016). The need for knowledge extraction: Understanding harmful gambling behavior with neural networks. .
  15. Uus, A., Liatsis, P., Slabaugh, G., Anagnostis, A., Roberts, S. and Twist, S. (2016). Trend Deviation Analysis for Automated Detection of Defects in GPR Data for Road Condition Surveys. .
  16. Slabaugh, G.G. and Al-Arif, S.M. (2015). Hough Forest-based Corner Detection for Cervical Spine Radiographs. Medical Image Understanding and Analysis 15-17 July.
  17. Basaru, R.R., Child, C., Alonso, E. and Slabaugh, G. (2015). Quantized Census for Stereoscopic Image Matching. .
  18. Asad, M., Gentet, E., Basaru, R.R. and Slabaugh, G. (2015). Generatinga 3D hand model from frontal color and range scans. .
  19. Al Arifi, S.M.M.R., Asad, M., Knapp, K., Gundry, M. and Slabaugh, G. (2015). Cervical Vertebral Corner Detection using Haar-like Features and Modified Hough Forest. .
  20. (2014). Medical Image Understanding and Analysis 2014. Medical Image Understanding and Analysis 9-11 July, London UK.
  21. Emrith, K., Slabaugh, G.G., Poullis, A., Groves, C. and Smith, M.L. (2014). Photometric Stereo Reconstruction for Surface Analysis of Mucosal Tissue. .
  22. Asad, M. and Slabaugh, G. (2014). Hand orientation regression using random forest for augmented reality. .
  23. Roth, H., Hampshire, T., McClelland, J., Hu, M., Boone, D., Slabaugh, G.G., Halligan, S. and Hawkes, D.J. (2011). Inverse Consistency Error in the Registration of Prone and Supine Images in CT Colonography. .
  24. Ye, X. and Slabaugh, G.G. (2011). Concavity analysis for reduction of ileocecal valve false positives in CTC. .
  25. Hampshire, T., Roth, H., Hu, M., Boone, D., Slabaugh, G.G., Punwani, S., Halligan, S. and Hawkes, D.J. (2011). Automatic Prone to Supine Haustral Fold Matching in CT Colonography Using a Markov Random Field Model. .
  26. Baloch, S., Melkisetoglu, R., Flöry, S., Azernikov, S., Slabaugh, G.G., Zouhar, A. and Fang, T. (2010). Automatic Detection of Anatomical Features on 3D Ear Impressions for Canonical Representation. .
  27. Roth, H., McClelland, J., Modat, M., Boone, D., Hu, M., Ourselin, S., Slabaugh, G.G., Halligan, S. and Hawkes, D.J. (2010). Establishing Spatial Correspondence between the Inner Colon Surfaces from Prone and Supine CT Colonography. .
  28. Yang, X., Beddoe, G. and Slabaugh, G.G. (2010). Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model. .
  29. Ye, X., Beddoe, G. and Slabaugh, G.G. (2010). A Bayesian Approach for False Positive Reduction in CTC CAD. .
  30. Boyes, R., Slabaugh, G.G. and Beddoe, G. (2009). Fast pseudo-enhancement correction in CT colonography using linear shift-invariant filters. .
  31. Ye, X., Siddique, M., Douiri, A., Beddoe, G. and Slabaugh, G.G. (2009). Shape-Based CT Lung Nodule Segmentation Using Five-Dimensional Mean Shift Clustering and Mem with Shape Information. .
  32. Unal, G., Nain, D., Slabaugh, G., Fang, T. and IEEE, (2009). 3-D Statistical Shape Modeling and Application to Prototyping of Hearing Aids. .
  33. Slabaugh, G.G., Unal, G.B., Fang, T., Rossignac, J. and Whited, B. (2008). Variational Skinning of an Ordered Set of Discrete 2D Balls. .
  34. Unal, G.B., Nain, D., Slabaugh, G.G. and Fang, T. (2008). Customized Design of Hearing Aids Using Statistical Shape Learning. .
  35. Wang, Z.W., Slabaugh, G.G., Zhou, M. and Fang, T. (2008). Automatic tracing of blood flow velocity in pulsed Doppler images. .
  36. Wang, Z.W., Slabaugh, G.G. and Fang, T. (2008). Partial differential equation-based GPR signature discrimination for automatic detection of bridge deck delamination. .
  37. Slabaugh, G.G., Dinh, H.Q. and Unal, G.B. (2007). A Variational Approach to the Evolution of Radial Basis Functions for Image Segmentation. .
  38. Wang, Z.W., Slabaugh, G.G., Unal, G.B. and Fang, T. (2007). Registration of Ultrasound Images Using an Information-Theoretic Feature Detector. .
  39. Slabaugh, G.G., Kong, K., Unal, G.B. and Fang, T. (2007). Variational Guidewire Tracking Using Phase Congruency. .
  40. Wang, Z.W., Slabaugh, G.G., Unal, G.B., Zhou, M. and Fang, T. (2007). An information-theoretic detector based scheme for registration of speckled medical images. .
  41. Wang, Z., Slabaugh, G., Unal, G., Zhou, M., Fang, T. and IEEE, (2007). An information-theoretic detector based scheme for registration of speckled medical images. .
  42. Slabaugh, G.G., Unal, G. and Chang, T. (2006). Information-Theoretic Feature Detection in Ultrasound Images. 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006 (EMBS '06) 30 Aug 2006 – 3 Sep 2006, New York, USA.
  43. Abufadel, A., Slabaugh, G.G., Unal, G.B., Zhang, L. and Odry, B. (2008). Interacting Active Rectangles for Estimation of Intervertebral Disk Orientation. 8th International Conference on Pattern Recognition (ICPR), 2006. 20 Aug 2006 – 24 Aug 2008, Hong Kong.
  44. Slabaugh, G.G., Unal, G., Tong Fang, and Wels, M. (2006). Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 17-22 June, New York, USA.
  45. Zouhar, A., Fang, T., Unal, G.B., Slabaugh, G.G., Xie, H. and McBagonluri, F. (2006). Anatomically-Aware, Automatic, and Fast Registration of 3D Ear Impression Models. .
  46. Slabaugh, G.G., Unal, G.B., Fang, T. and Wels, M. (2006). Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours. .
  47. Doan, H.-.N., Slabaugh, G.G., Unal, G.B. and Fang, T. (2006). Semi-Automatic 3-D Segmentation of Anatomical Structures of Brain MRI Volumes using Graph Cuts. .
  48. Unal, G.B., Slabaugh, G.G., Ess, A., Yezzi, A.J., Fang, T., Tyan, J., Requardt, M., Krieg, R., Seethamraju, R.T., Harisinghani, M. and Weissleder, R. (2006). Semi-Automatic Lymph Node Segmentation in LN-MRI. .
  49. Slabaugh, G., Unal, G., Chang, T.-.C. and IEEE, (2006). Information-theoretic feature detection in ultrasound images. .
  50. Unal, G. and Slabaugh, G.G. (2005). Coupled PDEs for Non-Rigid Registration and Segmentation. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 21-23 September, San Diego, USA.
  51. Slabaugh, G.G., Mihalef, V. and Unal, G. (2005). A Contour-Based Approach to 3D Text Labeling on Triangulated Surfaces. Fifth International Conference on 3-D Digital Imaging and Modeling, 2005 (3DIM 2005) 13-16 June, Ottowa, Canada.
  52. Slabaugh, G.G. and Unal, G.B. (2005). Active Polyhedron: Surface Evolution Theory Applied to Deformable Meshes. .
  53. Slabaugh, G. and Unal, G. (2005). Graph cuts segmentation using an elliptical shape prior. .
  54. Slabaugh, G., Unal, G. and IEEE, (2005). Graph cuts segmentation using an elliptical shape prior. .
  55. Slabaugh, G.G., Schafer, R.W. and Hans, M.C. (2002). Image-Based Photo Hulls. First International Symposium on 3D Data Processing Visualization and Transmission 19-21 June, Padova, Italy.
  56. Yezzi, A.J., Slabaugh, G.G., Broadhurst, A., Cipolla, R. and Schafer, R.W. (2002). A Surface Evolution Approach to Probabilistic Space Carving. First International Symposium on 3D Data Processing Visualization and Transmission 19-21 June, Padova, Italy.
  57. Slabaugh, G.G., Schafer, R.W. and Hans, M.C. (2002). Multi-resolution space carving using level set methods. .
  58. Slabaugh, G.G., Culbertson, B., Malzbender, T. and Schafer, R. (2001). A survey of methods for volumetric scene reconstruction from photographs. 2001 Eurographics conference on Volume Graphics 21-22 June, New York, USA.
  59. Dinh, H.Q., Turk, G. and Slabaugh, G.G. (2001). Reconstructing Surfaces Using Anisotropic Basis Functions. .
  60. Pollefeys, M., Kang, S.B., Slabaugh, G.G., Cornelis, K. and Debevec, P.E. (2000). Panel Session on Visual Scene Representation. .
  61. Lazareva, A., Asad, M. and Slabaugh, G. Learning to Deblur Adaptive Optics Retinal Images. International Conference on Image Analysis and Recognition Montreal, Canada.

Internet Publication

  1. Slabaugh, G.G. and Slabaugh, G.G. Home Page..

Journal Articles (56)

  1. Carlos Reyes-Aldasoro, C. and Slabaugh, G. (2017). Special issue: medical image understanding and analysis. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 5(5), p. 317. doi:10.1080/21681163.2015.1081080.
  2. Jawaid, M.M., Riaz, A., Rajani, R., Reyes-Aldasoro, C.C. and Slabaugh, G. (2017). Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles. Computers in biology and medicine, 89, pp. 84–95. doi:10.1016/j.compbiomed.2017.07.021.
  3. Jawaid, M.M., Rajani, R., Liatsis, P., Reyes-Aldasoro, C.C. and Slabaugh, G. (2017). A hybrid energy model for region based curve evolution – Application to CTA coronary segmentation. Computer Methods and Programs in Biomedicine, 144, pp. 189–202. doi:10.1016/j.cmpb.2017.03.020.
  4. Albrecht, T., Slabaugh, G., Alonso, E. and Al-Arif, M.R. (2017). Deep Learning for Single-Molecule Science. Nanotechnology . doi:10.1088/1361-6528/aa8334.
  5. Yang, G., Nawaz, T., Barrick, T.R., Howe, F.A. and Slabaugh, G. (2015). Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy. IEEE Transactions on Biomedical Engineering, 62(12), pp. 2860–2866. doi:10.1109/TBME.2015.2448232.
  6. Slabaugh, G. and Reyes-Aldasoro, C. (2015). Guest Editorial: Medical Image Understanding and Analysis. Annals of the British Machine Vision Association, 2015(1), pp. 1–2.
  7. Yang, X., Ye, X. and Slabaugh, G. (2015). Multilabel region classification and semantic linking for colon segmentation in CT colonography. IEEE Transactions on Biomedical Engineering, 62(3), pp. 948–959. doi:10.1109/TBME.2014.2374355.
  8. Jebri, B., Phillips, M., Knapp, K., Appelboam, A., Reuben, A. and Slabaugh, G. (2015). Detection of degenerative change in lateral projection cervical spine x-ray images. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 9414 . doi:10.1117/12.2082515.
  9. Narang, B., Phillips, M., Knapp, K., Appelboam, A., Reuben, A. and Slabaugh, G. (2015). Semi-automatic delineation of the spino-laminar junction curve on lateral X-ray radiographs of the cervical spine. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 9413 . doi:10.1117/12.2082036.
  10. Weyde, T., Slabaugh, G., Fontaine, G. and Bederna, C. (2013). Predicting aquaplaning performance from tyre profile images with machine learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7950 LNCS, pp. 133–142. doi:10.1007/978-3-642-39094-4_16.
  11. Boone, D.J., Halligan, S., Roth, H.R., Hampshire, T.E., Helbren, E., Slabaugh, G.G., McQuillan, J., McClelland, J.R., Hu, M., Punwani, S., Taylor, S.A. and Hawkes, D.J. (2013). CT colonography: external clinical validation of an algorithm for computer-assisted prone and supine registration. Radiology, 268(3), pp. 752–760. doi:10.1148/radiol.13122083.
  12. Vazquez, E., Yang, X. and Slabaugh, G.G. (2013). Erosion band signatures for spatial extraction of features. Mach. Vis. Appl., 24, pp. 695–705. doi:10.1007/s00138-012-0422-8.
  13. Hampshire, T., Roth, H., Helbren, E., Plumb, A., Boone, D., Slabaugh, G.G., Halligan, S. and Hawkes, D.J. (2013). Endoluminal surface registration for CT colonography using haustral fold matching. Medical Image Analysis, 17, pp. 946–958. doi:10.1016/j.media.2013.04.006.
  14. Ganz, M., Yang, X. and Slabaugh, G.G. (2012). Automatic Segmentation of Polyps in Colonoscopic Narrow-Band Imaging Data. IEEE Trans. Biomed. Engineering, 59, pp. 2144–2151. doi:10.1109/TBME.2012.2195314.
  15. Roth, H., Hampshire, T., McClelland, J., Hu, M., Boone, D.J., Slabaugh, G.G., Halligan, S. and Hawkes, D.J. (2012). Inverse Consistency Error in the Registration of Prone and Supine Images in CT Colonography. Lecture Notes in Computer Science, 7029, pp. 1–7. doi:10.1007/978-3-642-28557-8_1.
  16. Hampshire, T., Roth, H., Hu, M., Boone, D., Slabaugh, G., Punwani, S., Halligan, S. and Hawkes, D. (2011). Automatic prone to supine haustral fold matching in CT colonography using a Markov random field model. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 14(Pt 1), pp. 508–515.
  17. Wang, Z.W., Zhou, M., Slabaugh, G.G., Zhai, J. and Fang, T. (2011). Automatic detection of bridge deck condition from ground penetrating radar images. IEEE Transactions on Automation Science and Engineering, 8(3), pp. 633–640. doi:10.1109/TASE.2010.2092428.
  18. Roth, H.R., McClelland, J.R., Boone, D.J., Modat, M., Cardoso, M.J., Hampshire, T.E., Hu, M., Punwani, S., Ourselin, S., Slabaugh, G.G., Halligan, S. and Hawkes, D.J. (2011). Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography. Med Phys, 38(6), pp. 3077–3089. doi:10.1118/1.3577603.
  19. Ünal, G.B., Nain, D., Slabaugh, G.G. and Fang, T. (2011). Generating shapes by analogies: An application to hearing aid design. Computer-Aided Design, 43, pp. 47–56. doi:10.1016/j.cad.2010.09.008.
  20. Yang, X. and Slabaugh, G. (2011). A robust and efficient approach to detect 3D rectal tubes from CT colonography. Journal of Medical Physics, 38(11), p. 6238. doi:10.1118/1.3654842.
  21. Yang, X., Beddoe, G. and Slabaugh, G.G. (2011). Learning to Detect 3D Rectal Tubes in CT Colonography Using a Global Shape Model. Lecture Notes in Computer Science, 6668, pp. 53–59. doi:10.1007/978-3-642-25719-3_8.
  22. Ye, X., Beddoe, G. and Slabaugh, G.G. (2011). A Bayesian Approach for False Positive Reduction in CTC CAD. Lecture Notes in Computer Science, 6668, pp. 40–46. doi:10.1007/978-3-642-25719-3_6.
  23. Baloch, S., Melkisetoglu, R., Flöry, S., Azernikov, S., Slabaugh, G., Zouhar, A. and Fang, T. (2010). Automatic detection of anatomical features on 3D ear impressions for canonical representation. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 13(Pt 3), pp. 555–562.
  24. Roth, H., McClelland, J., Modat, M., Boone, D., Hu, M., Ourselin, S., Slabaugh, G., Halligan, S. and Hawkes, D. (2010). Establishing spatial correspondence between the inner colon surfaces from prone and supine CT colonography. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 13(Pt 3), pp. 497–504.
  25. Slabaugh, G., Boyes, R. and Yang, X. (2010). Multicore image processing with openMP. IEEE Signal Processing Magazine, 27(2), pp. 134–138. doi:10.1109/MSP.2009.935452.
  26. Slabaugh, G., Yang, X., Ye, X., Boyes, R. and Beddoe, G. (2010). A Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions. Algorithms, 3(1), pp. 21–43.
  27. Slabaugh, G.G., Whited, B., Rossignac, J., Fang, T. and Unal, G.B. (2010). 3D ball skinning using PDEs for generation of smooth tubular surfaces. Computer-Aided Design, 42, pp. 18–26. doi:10.1016/j.cad.2009.03.004.
  28. Ye, X., Beddoe, G. and Slabaugh, G.G. (2010). Automatic Graph Cut Segmentation of Lesions in CT Using Mean Shift Superpixels. Int. J. Biomedical Imaging, 2010, pp. 983963:1–983963:1. doi:10.1155/2010/983963.
  29. Yang, X., Tek, B., Beddoe, G. and Slabaugh, G. (2010). Feature selection for computer-aided polyp detection using MRMR. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 7624 . doi:10.1117/12.844165.
  30. Douiri, A., Siddique, M., Ye, X., Beddoe, G. and Slabaugh, G. (2009). Enhanced detection in CT colonography using adaptive diffusion filtering. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 7259 . doi:10.1117/12.811563.
  31. Ye, X., Siddique, M., Douiri, A., Beddoe, G. and Slabaugh, G. (2009). Image segmentation using joint spatial-intensity-shape features: Application to CT lung nodule segmentation. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 7259 . doi:10.1117/12.811151.
  32. Ünal, G., Nain, D., Slabaugh, G. and Fang, T. (2009). 3-D statistical shape modeling and application to prototyping of hearing aids. 2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009 pp. 952–955. doi:10.1109/SIU.2009.5136555.
  33. Whited, B., Rossignac, J., Slabaugh, G., Fang, T. and Unal, G. (2009). Pearling: Stroke segmentation with crusted pearl strings. Pattern Recognition and Image Analysis, 19(2), pp. 277–283. doi:10.1134/S1054661809020102.
  34. Slabaugh, G., Unal, G., Wels, M., Fang, T. and Rao, B. (2009). Statistical Region-Based Segmentation of Ultrasound Images. Ultrasound in Medicine and Biology, 35(5), pp. 781–795. doi:10.1016/j.ultrasmedbio.2008.10.014.
  35. Ye, X., Lin, X., Dehmeshki, J., Slabaugh, G.G. and Beddoe, G. (2009). Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images. IEEE Trans. Biomed. Engineering, 56, pp. 1810–1820. doi:10.1109/TBME.2009.2017027.
  36. Whited, B., Rossignac, J., Slabaugh, G., Fang, T. and Unal, G. (2008). Pearling: Stroke segmentation with crusted pearl strings. Proceedings of the 1st International Workshop on Image Mining Theory and Applications, IMTA 2008 - In Conjunction with VIISIGRAPP 2008 pp. 103–112.
  37. Gucunski, N., Slabaugh, G., Wang, Z., Fang, T. and Maher, A. (2008). Impact echo data from bridge deck testing: Visualization and interpretation. Transportation Research Record, (2050), pp. 111–121. doi:10.3141/2050-11.
  38. Unal, G., Nain, D., Slabaugh, G. and Fang, T. (2008). Customized design of hearing aids using statistical shape learning. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11(Pt 1), pp. 518–526.
  39. Ünal, G. and Slabaugh, G. (2008). Guidewire tracking in x-ray videos of endovascular interventions. 2008 IEEE 16th Signal Processing, Communication and Applications Conference, SIU . doi:10.1109/SIU.2008.4632624.
  40. Slabaugh, G., Fang, T., McBagonluri, F., Zouhar, A., Melkisetoglu, R., Xie, H. and Unal, G. (2008). 3-D shape modeling for hearing aid design. IEEE Signal Processing Magazine, 25(5), pp. 98–102. doi:10.1109/MSP.2008.926653.
  41. Odry, B.L., Kiraly, A.P., Slabaugh, G.G., Novak, C.L., Naidich, D.P. and Lerallut, J.F. (2008). Active contour approach for accurate quantitative airway analysis. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 6916 . doi:10.1117/12.772592.
  42. Pauly, O., Unal, G., Slabaugh, G., Carlier, S. and Fang, T. (2008). Semi-automatic matching of OCT and IVUS images for image fusion. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 6914 . doi:10.1117/12.773805.
  43. Unal, G.B. and Slabaugh, G.G. (2008). Estimation of Vector Fields in Unconstrained and Inequality Constrained Variational Problems for Segmentation and Registration. Journal of Mathematical Imaging and Vision, 31, pp. 57–72. doi:10.1007/s10851-008-0064-7.
  44. Unal, G.B., Bucher, S., Carlier, S.G., Slabaugh, G.G., Fang, T. and Tanaka, K. (2008). Shape-Driven Segmentation of the Arterial Wall in Intravascular Ultrasound Images. IEEE Trans. Information Technology in Biomedicine, 12, pp. 335–347. doi:10.1109/TITB.2008.920620.
  45. Unal, G.B., Slabaugh, G.G., Kakadiaris, I.A. and Tannenbaum, A. (2008). Guest Editorial Introduction to the Special Section on Computer Vision for Intravascular and Intracardiac Imaging. IEEE Trans. Information Technology in Biomedicine, 12, pp. 273–276. doi:10.1109/TITB.2008.920458.
  46. Slabaugh, G.G., Unal, G., Fang, T., Rossignac, J. and Whited, B. (2008). Variational Skinning of an Ordered Set of Discrete 2D Balls. Lecture Notes in Computer Science, 4975(2008), pp. 450–461. doi:10.1007/978-3-540-79246-8_34.
  47. Unal, G., Nain, D., Slabaugh, G.G. and Fang, T. (2008). Customized Design of Hearing Aids Using Statistical Shape Learning. Lecture Notes in Computer Science, 5241(2008), pp. 518–526. doi:10.1007/978-3-540-85988-8_62.
  48. Slabaugh, G., Kong, K., Unal, G. and Fang, T. (2007). Variational guidewire tracking using phase congruency. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4792 LNCS(PART 2), pp. 612–619.
  49. Alvino, C.V., Unal, G.B., Slabaugh, G.G., Peny, B. and Fang, T. (2007). Efficient segmentation based on Eikonal and diffusion equations. Int. J. Comput. Math., 84, pp. 1309–1324. doi:10.1080/00207160701324249.
  50. Unal, G.B., Yezzi, A.J., Soatto, S. and Slabaugh, G.G. (2007). A Variational Approach to Problems in Calibration of Multiple Cameras. IEEE Trans. Pattern Anal. Mach. Intell., 29, pp. 1322–1338. doi:10.1109/TPAMI.2007.1035.
  51. Slabaugh, G.G., Culbertson, W.B., Malzbender, T., Stevens, M.R. and Schafer, R.W. (2004). Methods for Volumetric Reconstruction of Visual Scenes. International Journal of Computer Vision, 57, pp. 179–199. doi:10.1023/B:VISI.0000013093.45070.3b.
  52. Slabaugh, G.G., Schafer, R.W. and Hans, M.C. (2003). Image-based photo hulls for fast and photo-realistic new view synthesis. Real-Time Imaging, 9, pp. 347–360. doi:10.1016/j.rti.2003.08.004.
  53. Dinh, H.Q., Turk, G. and Slabaugh, G.G. (2002). Reconstructing Surfaces by Volumetric Regularization Using Radial Basis Functions. IEEE Trans. Pattern Anal. Mach. Intell., 24, pp. 1358–1371. doi:10.1109/TPAMI.2002.1039207.
  54. Al Arif, S.M.M.R., Asad, M., Gundry, M., Knapp, K. and Slabaugh, G.G. Patch-based Corner Detection for Cervical Vertebrae in X-ray Images. Signal Processing: Image Communication .
  55. Asad, M. and Slabaugh, G.G. Staged Probabilistic Regression for Hand Orientation Inference. Computer Vision and Image Understanding .
  56. Yu, S., Dong, H., Yang, G., Slabaugh, G., Dragotti, P.L., Ye, X., Liu, F., Arridge, S., Keegan, J., Firmin, D. and Guo, Y. Deep De-Aliasing for Fast Compressive Sensing MRI. IEEE Transactions on Medical Imaging .

Education

Education

PhD, Electrical Engineering, highest honours, Georgia Institute of Technology


MSc, Electrical Engineering, highest honours, Georgia Institute of Technology


BSc, Engineering Physics, highest honours, University of Michigan

Teaching

Dr Slabaugh is the director of the MSc Computer Games Technology course at City University London. He regularly teaches modules Computer Graphics INM376/IN3005 and Computer Vision INM460/IN3060. He also contributes to teaching Java to first year computer science students, with teaching in the Java Bootcamp and Introduction to Java (IN1007).

Other Activities

Collaborations (Industrial) (2)

  1. of
    Other partners: Avascope, Ltd
  2. of

Editorial Activity

  1. Associate Editor, IEEE Signal Processing Magazine, 2007 - 2012.

Event/Conference

  1. Co-chair of the Medical Image Analysis and Understanding (MIUA) conference, 2014. Royal Holloway (2014).
    Description: http://www.city.ac.uk/medical-image-understanding-and-analysis-2014

Find us

City, University of London

Northampton Square

London EC1V 0HB

United Kingdom

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City, University of London is an independent member institution of the University of London. Established by Royal Charter in 1836, the University of London consists of 18 independent member institutions with outstanding global reputations and several prestigious central academic bodies and activities.