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Jundong Liu

Associate Professor
Biomedical Engineering, Electrical Engineering and Computer Science, Center for Scientific Computing and Immersive Technologies
Stocker Center 321A
Phone: 740.593.1603

Jundong Liu is an associate professor in the School of Electrical Engineering and Computer Science at Ohio University, and the director of the Medical Image Analysis (MedIA) Lab at Ohio University. He researches medical image analysis, machine learning, computer vision, human motion detection and classification, and shape modeling.

Research Interests: electrical engineering and computer science, medical image analysis, computer vision, computer graphics

All Degrees Earned: Ph.D., Computer Information Science and Engineering, University of Florida; M.S., Computer Science, Peking University

Technical Report (1)

  • Shi, B., , . (2015). Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations. Computing Research Repository;

Conference Proceeding (46)

Journal Article, Academic Journal (17)

  • Chen, Y., Wang, Z., Smith, C., Liu, J. (2018). Accurate Brain Tumor Segmentation through Sequentially Coupled ConvNets. Medical Physics.
  • Chen, Y., Wang, Z., Shi, B., Sun, T., Zhang, P., Smith, C., Liu, J. (2018). Adaptive Convolutional Neural Networks for Three-Dimensional Hippocampus Segmentation . IEEE Transactions on Image Processing.
  • Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. (2018). Quad-mesh based Subcortical Shape Analysis for Alzheimer's Disease. IEEE Journal of Biomedical and Health Informatics.
  • Wang, Z., Chen, Y., Smith, C., Liu, J. (2018). Robust White Matter Hypter Intensity (WMHI) Detection through Multi-Scale Neural Networks. Medical Physics.
  • Zhang, P., Shi, B., Smith, C., Liu, J. (2017). Learning Feature Transformations to Improve Semi-Supervised Classification. Pattern Recognition.
  • Shi, B., Liu, J. (2017). Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations. Neurocomputing.
  • Shi, B., Chen, Y., Zhang, P., Smith, C., Liu, J. (2017). Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis. Frankfurt, D60486 Germany: Pattern Recognition ; 63: pp. 487-498.
  • Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. Quad-mesh Coordinate Modeling and its applications in Neuroimages. computerized graphics medical imaging.
  • Colvin, R., Liu, J. (2012). Proceedings from the Great Lakes Bioinformatics Conference 2011. Preface. BMC Bioinformatics; 13 Suppl 2: I1.
  • Liu, J., Colvin, R. (2012). Preface. S-2. BMC Bioinformatics; 13: I1.
  • Liu, J., Chelberg, D., Smith, C., Chebrolu, H. (2009). A Local Likelihood-based Level Set Segmentation Method for Brain MR Images. F09. International Journal of Tomography and Statistics; 12:
  • Smith, C., Chebrolu, H., Markesbery, W., Liu, J. (2008). Improved predictive model for pre-symptomatic mild cognitive impairment and Alzheimer's disease. 10. Neurological Research; 30: 1091-1096.
  • Liu, J., wang, y. (2008). Segmentation-Assisted Image Registration for Brain Morphological Analysis. 5. International Journal of Computational Science; 2: 690-707.
  • Li, C., Liu, J., Fox, M. (2005). Segmentation of External Force Field for Automatic Initialization and Splitting of Snakes. 11. Pattern Recognition; 38: 1947-1960.
  • Cao, L., Harrington, P., Liu, J. (2005). SIMPLISMA and ALS Applied to Two-dimensional Nonlinear Wavelet Compressed Ion Mobility Spectra of Chemical Warfare Agent Simulants. 8. Analytic Chemistry; 77: 2575-2586.
  • Liu, J., Vemuri, B., Bova, F. (2002). Efficient Multimodal Image Registration using Local Frequency Maps. 3. Secaucus, NJ: Machine Vision and Application/Springer-Verlag New York Inc.; 13: 149-163.
  • Liu, J., Vemuri, B., Marroquin, J. (2002). Local Frequency Representation for Robust Multi-modal Image Registration. 5. IEEE Transactions on Medical Imaging; 21: 462-469.

Book, Chapter in Scholarly Book (5)

  • Liu, J. (2011). Segmentation-Assisted Registration for Brain MR Images. Springer Science ;
  • Liu, J. (2008). A Unified Framework for Segmentation-assisted Image Registration. 14. Recent Advances in Computational Sciences, Jorgensen/ Shen/Shu/Yan eds. / World Scientific; 1: 243-254.
  • Liu, J., Wang, Y. (2008). A Unified Framework for Segmentation-assisted Image Registration,. World Scientific; 243-254.
  • Liu, J. (2007). Deformable Model-based Image Registration. Springer; 1: 517-542.
  • Liu, J. (2007). 15. Deformable Models: Biomedical and Clinical Applications, Suri/Farag, eds.,; 1: 517-542.

Conference, Poster (6)

  • Xie, S., Liu, J. (2012). A Novel Riemannian Shape Analysis Framework for Subcoritcal Brain Structures. 2012 Annual Meeting of Biomedical Enegineering Society;
  • Shi, B., Liu, J. (2012). Registration-based segmentation of intra-abdominal and subcutaneous adipose tissue in 3D mouse micro-CT. 2012 Annual Meeting of Biomedical Enegineering Society.
  • Xu, H., Liu, J. (2012). Robust Point Registration Using Clusters and Generalized Radial Basis Functions. 2012 Annual Meeting of Biomedical Enegineering Society.
  • Liu, J. (2011). Artifacts in Mutual Information‑based Image Registration: Analysis and Remedy. Great Lakes Bioinformatics Conference 2011;
  • Liu, J., Smith, C., Chebrolu, H. (2011). Automatic Multiple Sclerosis detection Based on integrated Square Estimation. Great Lakes Bioinformatics Conference 2011;
  • Liu, J., Shi, B. (2011). Regularity Guaranteed Deformation Estimation in Image Registration. Great Lakes Bioinformatics Conference 2011;


  • Zhu, J., Wilhelm, J., Williams II, R., Uijt de Haag, M., Bartone, C., Liu, J., Chelberg, D., Liu, C., DiBenedetto, M. An Integrated, Scalable All-Weather, All-Terrain, All-Time, Autonomous Perimeter Monitoring and Ground Inspection System, Provisional patent application. OU16018.