Yongyi Yang

Education

Ph.D., Illinois Institute of Technology, 1994
M.S., AM, Illinois Institute of Technology,1992
M.S.E.E., Northern Jiaotong University, Beijing, China,1988
B.S.E.E., Northern Jiaotong University, Beijing, China,1985

Research Interests

Yongyi Yang has research interests in the fields of image processing, medical imaging, machine learning, color vision, and optical information processing. He has authored or co-authored over 300 publications in these areas. His current research topics include: 1) tomographic image reconstruction methods for cardiac and pediatric kidney imaging, 2) Computer-aided diagnosis techniques for breast cancer detection in mammography, 3) Modeling of image similarity from human observers for content-based image retrieval, 4) Construction of brain atlas of adults without dementia for neuroimaging studies of age-related neurological diseases, 5) Multispectral infrared imaging of the iris for diagnostic ophthalmology, and 6) Optimization of the use of video technologies in policing.

Yang is currently a Senior Associate Editor for IEEE Transactions on Image Processing. He has also been an Associate Editor for IEEE Transactions on Image Processing, and a Guest Editor for multiple special issues of Pattern Recognition and IEEE Transactions on Selected Topics for Signal Processing. He has served on the IEEE Bio Imaging and Signal Processing (BISP) Technical Committee. Dr. Yang is also a frequent participant in NIH study sections and NSF review panels. He chaired a study section for NIH P41 Biomedical Technology Resource Centers in 2015. He is a Fellow of IEEE and AMIBE.

Awards

• Fellow, Institute of Electrical and Electronics Engineers (IEEE), "For contributions to medical image recovery and analysis," 2021.
• Fellow, American Institute for Medical and Biological Engineering (AIMBE), “For outstanding contributions to medical image processing and analysis,” 2018.
• Sigma Xi Research Award, Senior Faculty Division, IIT, 2010.
• Best Student Paper Award, IEEE Inter. Symp. on Biomedical Imaging: from Nano to Macro, 2014.
• Top 10% Paper Recognition, IEEE Inter. Conf. on Image Processing, 2015.
• Paper recognition: Physics in Medicine and Biology, Highlights collection of 2011 (“for their presentation of outstanding new research, receipt of the highest praise from our international referees and the highest number of downloads last year”), 2011.
• Paper recognition: Physics in Medicine and Biology, two papers recognized by Institute of Physics Select (“for their novelty, significance and potential impact on future research”), 2006.
• Best Student Paper Award, IEEE Medical Imaging Conference, 2006.
• Honorable Mention Poster Award (one of three), SPIE International Symposium Medical Imaging, 2005.

Publications

Image and signal recovery
[1] Y. Yang, N. P. Galatsanos, and A. K. Katsaggelos, “Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images,” IEEE Trans. on Circuits and Systems for Video Tech., vol. 3, no. 6, pp. 421-432, 1993.

[2] Y. Yang, N. P. Galatsanos, and H. Stark, “Projection-based blind deconvolution,” J. Opt. Soc. Am. A, vol. 11, no. 9, pp. 2401-2409, 1994.

[3] Y. Yang, N. Galatsanos, and A. Katsaggelos, “Projection-based spatially-adaptive reconstruction of block transform compressed images,” IEEE Trans. on Image Processing, vol. 4, no. 7, pp. 896-908, 1995.

[4] Y. Yang and N. P. Galatsanos, “Removal of compression artifacts using projections onto convex sets and line process modeling,” IEEE Trans. on Image Processing, vol. 6, no. 10, pp. 1345-1357, 1997.

[5] Y. Yang and H. Stark, “Design of self-healing arrays using vector space projections,” IEEE Trans. on Antennas and Propagation, vol. 49, no. 4, pp. 526-534, 2001.

[6] M. Choi, Y. Yang, and N. P. Galatsanos, “Multichannel regularized recovery of compressed video sequences,” IEEE Trans. on Circuits and Systems II, vol. 48, no. 4, pp. 376-387, 2001.

[7] Y. Yang, J. Brankov, and M. Wernick, “A computationally efficient approach for accurate content-adaptive mesh generation,” IEEE Trans. on Image Processing, vol. 12, no. 8, pp. 866-881, 2003.

[8] J. Brankov, Y. Yang, M. N. Wernick, “Content-adaptive mesh modeling for tomographic image reconstruction,” IEEE Trans. on Medical Imaging, vol. 23, pp. 202-212, 2004.

[9] P. Dong, J. Brankov, N. P. Galatsanos, Y. Yang, and F. Davoine, “Digital watermarking robust to geometric distortions,” IEEE Trans. on Image Processing, vol. 14, pp. 2140-2150, 2005.

[10] M. N. Wernick, Y. Yang, I. Mondal, D. Chapman, M. Hasnah, C. Parham, E. Pisano and Z. Zhong, “Computation of mass-density images from x-ray refraction-angle images,” Phys. Med. Biol., vol. 51, pp. 1769-1778, 2006.

Spatiotemporal (4D) image reconstruction
[11] E. Gravier and Y. Yang, “Motion-compensated reconstruction of tomographic image sequences,” IEEE Trans. on Nuclear Science, vol. 52, pp. 51-56, 2005.

[12] E. Gravier, Y. Yang, M. A. King, and M. Jin, “Fully 4D motion-compensated reconstruction of cardiac SPECT images,” Phys. Med. Biol., vol. 51, pp. 4603-4619, 2006.

[13] M. Jin, Y. Yang, and M. A. King, “Reconstruction of dynamic gated cardiac SPECT,” Medical Physics, vol. 33, pp. 4384-4394, 2006.

[14] E. Gravier, Y. Yang, and M. Jin, “Tomographic reconstruction of dynamic cardiac image sequences,” IEEE Trans. on Image Processing, vol. 16, pp. 932-942, 2007.

[15] J. G. Brankov, Y. Yang, L. Wei, I. El-Naqa, and M. N. Wernick, “Learning a channelized observer for image quality assessment,” IEEE Trans. on Medical. Imaging, vol. 28, pp. 991- 999, 2009.

[16] X. Niu, Y. Yang, M. Jin, M. N. Wernick, and M. A. King, “Regularized fully 5D reconstruction of cardiac gated dynamic SPECT images,” vol. 57, pp.1085-1095, IEEE Trans. on Nuclear Science, vol. 57, no. 3, pp.1085-1095, 2010.

[17] L. Li and Y. Yang, “Optical flow estimation for a periodic image sequence,” IEEE Trans. on Image Processing, vol. 19, pp.1-10, 2010.

[18] X. Niu, Y. Yang, M. Jin, M. N. Wernick, and M. A. King, “Effects of motion, attenuation, and scatter corrections on gated cardiac SPECT reconstruction,” Medical Physics, vol. 38, no. 12, pp.6571-6584, 2011.

[19] W. Qi, Y. Yang, X. Niu, M. A. King, “A quantitative study of motion estimation methods on 4D cardiac gated SPECT reconstruction,” Medical Physics, vol. 39, no. 8, pp. 5182-5193, 2012.

[20] M. Jin, X. Niu, W. Qi, Y. Yang, J. Dey, M. A. King, S. Dahlberg, and M. N. Wernick, “4D reconstruction for low-dose cardiac gated SPECT,” Medical Physics, vol. 40, no. 2, 2013.

[21] W. Qi, Y. Yang, M. N. Wernick, P. H. Pretorius, and M. A. King, “Limited-angle effect compensation for respiratory binned cardiac SPECT,” Medical Physics, vol. 43, pp. 443-,2015.

[22] A. J. Ramon, Y. Yang, P. H. Pretorius, P. J. Slomka, K. L. Johnson, M. A. King, M. N. Wernick, “Investigation of dose reduction in cardiac perfusion SPECT via optimization and choice of the image reconstruction strategy,” Journal of Nuclear Cardiology, pp.1-11, 2017.

[23] W. Qi, Y. Yang, C. Song, M. N. Wernick, P. H. Pretorius, and M. A. King, “4D reconstruction with respiratory correction for gated myocardial perfusion SPECT,” IEEE Trans. on Medical Imaging, vol. 36, no. 8, pp. 1626-1635, 2017.

Machine learning methods for computer-aided diagnosis
[24] I. El-Naqa, Y. Yang, M. Wernick, N. P. Galatsanos, and R. Nishikawa, “A support vector machine approach for detection of microcalcifications,” IEEE Trans. on Medical Imaging, vol. 21, no. 12, pp. 1552-1563, 2002.

[25] I. El-Naqa, Y. Yang, N. P. Galatsanos, and M. Wernick, “A similarity learning approach to content based image retrieval: application to digital mammography,” IEEE Trans. on Medical Imaging, vol. 23, pp. 1233-1244, 2004.

[26] L. Wei, Y. Yang, R. M. Nishikawa, and Y. Jiang, “A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications,” IEEE Trans. on Medical Imaging, vol. 24, pp. 371-380, 2005.

[27] L. Wei, Y. Yang, R. M. Nishikawa, M. N. Wernick, and Alexandra Edwards, “Relevance vector machine for automatic detection of clustered microcalcifications,” IEEE Trans. on Medical Imaging, vol. 24, pp. 1278-1285, 2005.

[28] J. Tang, R. M. Rangayyan, J. Xu, I. El Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: recent advances,” IEEE Trans.Information Technology in Biomedicine, vol. 13, pp. 236-251, 2009.

[29] L. Wei, Y. Yang, M. N. Wernick, and R. M. Nishikawa, “Learning of perceptual similarity from expert readers for mammogram retrieval,” IEEE Journal of Selected Topics in Signal Processing, vol. 3, pp. 53-61, 2009.

[30] L. Wei, Y. Yang, and R. M. Nishikawa, “Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis,” Pattern Recognition, vol. 42, pp. 1126-1132, 2009.

[31] X. Liu, I. S. Yetik, D. L. Langer, M. A. Haider, Y. Yang, and Miles N. Wernick, “Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and classes,” IEEE Trans. on Medical. Imaging, vol. 28, pp. 906-915, 2009.

[32] H. Jing, Y. Yang, and R. M. Nishikawa, “Detection of clustered microcalcifications using spatial point process modeling,” Phys. Med. Biol., vol. 56, no.1, pp.1-17, 2011.

[33] H. Jing, Y. Yang, and R. M. Nishikawa, “Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer,” Medical Physics, vol. 39, no. 2, pp.676-85, 2012.

[34] J. Wang, H. Jing, M. N. Wernick, R. M. Nishikawa, and Y. Yang, “Analysis of perceived similarity between pairs of microcalcification clusters in mammograms,” Medical Physics, vol. 41(5):051904, 2014.

[35] J. Wang, R. M. Nishikawa, and Y. Yang, “Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model,” Medical Physics, vol. 43, pp. 159-,2016.

[36] J. Wang, R. M. Nishikawa, Y. Yang, “Global detection approach for clustered microcalcifications in mammograms using a deep learning network,” Journal of Medical Imaging, vol. 4, no. 2, pp. 024501-, 2017.

[37] M. V. Sainz de Cea, R. M. Nishikawa, and Y. Yang, “Estimating the accuracy level among individual detections in clustered microcalcifications,” IEEE Trans. Medical Imaging, vol. 36, no. 5, pp. 1162-1171, 2017.

[38] M. V. Sainz de Cea, R. M. Nishikawa, and Y. Yang, “Locally adaptive decision in detection of clustered microcalcifications in mammograms,” Physics in Medicine and Biology, 2017. Accepted.

[39] R. M. Nishikawa1, Y. Yang, J. Wang, A. Edwards, M. N. Wernick, J. Papaioannou, “Improving radiologists ability to discriminate mammographic calcifications using retrieved similar images,” Academic Radiology, 2017. Accepted.

Books

H. Stark and Y. Yang, Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics, John Wiley & Sons, Inc., New York, 1998.

Expertise

Image and signal recovery, tomographic image reconstruction, computer-aided diagnosis, machine learning, applied mathematical and statistical methods.