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Academic Areas: Image processing, Pattern recognition, Inverse problem, Sparse representation,Medical physics

Research Interests:

Shan Tan is a professor of the Department of Intelligent Science and Technology, School of Automation. He is a member of Key Laboratory of Ministry of Education for Image Processing and Intelligence Control, and Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of P. R. China.

Professor Tan's research is focused primarily on biomedical image processing and analysis, pattern recognition, and computer vision. His current research activities include automatic functional volume delineation in PET/CT imaging, CBCT reconstruction beyond total-variation (TV) regularization, and inverse problem (de-convolution and restoration) in super high-resolution fluorescence microscopy imaging.

Academic Degrees

Ph. D. in Pattern Recognition and Intelligent Systems, 09/2002-03/2007, School of Electronic Engineering, Xidian University, Xi’an, Shannxi Province, China 
M. Sc. in Electrical Engineering, 09/1999-06/2002, School of Electrical Engineering, Wuhan University, Wuhan, Hubei Province, China


Professional Experience

Professor, 06/2011- present, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
Postdoctoral Fellow, 06/2010-09/2011, Department of Radiation Oncology, School of Medicine, University of Maryland, Baltimore, MD
Postdoctoral Fellow, 06/2007-06/2010, Department of Computer Science, University of Houston, Houston, TX

Selected Publications

1. T. Sun, N. Sun, J. Wang, and S. Tan, “Iterative CBCT reconstruction using Hessian penalty”, Physics in medicine and biology, vol. 60, pp. 1965-1987, Feb 12 2015
2. H. Zhang, S. Tan, W. Chen, S. Kligerman, G. Kim, W. D. D'Souza, M. Suntharalingam, and W. Lu, “Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics”, International journal of radiation oncology, biology, physics, vol. 88, pp. 195-203, Jan 1 2014.
3. S. Tan, H. Zhang, Y. Zhang, W. Chen, W. D. D'Souza, and W. Lu, “Predicting Pathologic Tumor Response to Chemoradiotherapy with Histogram Distances Characterizing Longitudinal Changes in 18F-FDG Uptake Patterns”, Medical physics, vol. 40, no. 10, pp. 101707, Oct, 2013. (Featured Article)
4. S. Tan, S. Kligerman, W. Chen, M. Lu, G. Kim, S. Feigenberg, W. D. D'Souza, M. Suntharalingam, and W. Lu, “Spatial-Temporal 18F FDG-PET Features for Predicting Pathologic Response of Esophageal Cancer to Neoadjuvant Chemoradiation Therapy”, International Journal of Radiation Oncology Biology Physics, vol. 85, no. 5,pp. 1375-1382, 2013.
5. S. Tan and I. A. Kakadiaris, “Kernel active contour,” In: Proceedings of the 12th IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, Oct. 1-4, 2009.
6. S. Tan, L. Jiao, and I. A. Kakadiaris, “Wavelet-based Bayesian image estimation: from marginal and bivariate prior models to multivariate prior models,” IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 469-481, 2008.
7. S. Tan and L. Jiao, “A unified iterative denoising algorithm based on natural image statistical models: derivation and examples,” Optics Express, vol. 16, no.2, pp. 975-992, 2008.
8. S. Tan and L. Jiao, “Multishrinkage: analytical form for a Bayesian wavelet estimator based on the multivariate Laplacian model,” Optics Letters, vol. 32, no.17, pp. 2583-2585, 2007.
9. S. Tan and L. Jiao, “Multivariate statistical models for image denoising in the wavelet domain,” International Journal of Computer Vision, vol. 75, no. 2, pp. 209-230, 2007.
10. S. Tan and L. Jiao, “Ridgelet bi-frame,” Applied and Computational Harmonic Analysis, vol. 20, no. 3, pp. 391-402, 2006.
11. S. Tan and L. Jiao, “Image denoising using the Ridgelet bi-frame,” Journal of the Optical Society of America A: Optics, Image Science, and Vision, vol. 23, no. 10, pp. 2449-2461, 2006.


Awards and Honors

1. Honorable Mention of the 2009 Chinese Excellent Dissertation Award (2009)
2. Excellent Dissertation Award from Xidian University, China (2008)

Courses Taught

For Undergraduates: 
 1 Digital Image Process 
 2. An Introduction to Scientific Research

For Graduates:
 1. Pattern recognition and intelligent system


1. 1/2014 – 12/2017, "Research on Theory and Algorithms for PET Image Blind Segmentation”, National Natural Science Foundation of China (NNSFC), Grant No. 61375018, PI: Shan Tan.
2. 5/2012 – 5/2014, “Automatic Tumor Segmentation in PET Imaging,” Fundamental Research Funds for the Central Universities, Grant No. 2012QN086, PI: Shan Tan.
3. 1/2010 – 12/2012, “Adaptive Sparse Signal Representation and Reconstruction Based on Compressive Sensing,” National Natural Science Foundation of China (NNSFC), Grant No. 60971112, PI: Shan Tan.

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