Jundong Liu

Journal Article, Academic Journal (26)

• Di Caterina, G., Zhang, M., Liu, J. (2024). Theoretical advances and practical applications of spiking neural networks. 2023. Frontiers on Neuroscience; 18.
• Yue, Y., Baltes, M., Abuhajar, N., Sun, T., Karanth , A., Smith, C., Bihl, T., Liu, J. (2023). Spiking neural networks fine-tuning for brain image segmentation. 2023. Frontiers on Neuroscience; 17: https://www.frontiersin.org/articles/10.3389/fnins.2023.1267639/full.
• Sun, T., Gong, S., Wang, Z., Smith, C., Wang, X., Xu, L., Liu, J. (2021). Boosting the Intelligibility of Time-domain Speech Enhancement Networks through Speech Representations. IEEE Access.
• Liu, J., Muturi, H., Khuder, S., Helal, R., Ghadieh, H., Ramakrishnan, S., Kaw, M., Lester, S., Al-Khudhair, A., Conran, P., Chin, K., Gatto-Weis, C., Najjar, S. (2020). Loss of Ceacam1 promotes prostate cancer progression in Pten haploinsufficient male mice.. Metabolism: clinical and experimental; 107: 154215.
• Shi, B., Liu, J. (2018). Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations. Neurocomputing; http://www.sciencedirect.com/science/article/pii/S092523121830910X.
• Huang, L., Liu, J., Zhang, X., Sibley, K., Najjar, S., Lee, M., Wu, Q. (2018). Inhibition of protein arginine methyltransferase 5 enhances hepatic mitochondrial biogenesis.. 28. The Journal of biological chemistry; 293: 10884-10894.
• 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., 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. http://www.sciencedirect.com/science/article/pii/S0031320316302916.
• Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. Quad-mesh Coordinate Modeling and its applications in Neuroimages. computerized graphics medical imaging.
• Liu, J., Ramakrishnan, S., Khuder, S., Kaw, M., Muturi, H., Lester, S., Lee, S., Fedorova, L., Kim, A., Mohamed, I., Gatto-Weis, C., Eisenmann, K., Conran, P., Najjar, S. (2015). High-calorie diet exacerbates prostate neoplasia in mice with haploinsufficiency of Pten tumor suppressor gene.. 3. Molecular metabolism; 4: 186-98.
• Arum, O., Boparai, R., Saleh, J., Wang, F., Dirks, A., Turner, J., Kopchick, J., Liu, J., Khardori, R., Bartke, A. (2014). Specific suppression of insulin sensitivity in growth hormone receptor gene-disrupted (GHR-KO) mice attenuates phenotypic features of slow aging.. 6. Aging cell; 13: 981-1000.
• Colvin, R., Liu, J. (2012). Proceedings from the Great Lakes Bioinformatics Conference 2011. Preface. BMC Bioinformatics; 13 Suppl 2: I1.
• Chen, X., Gu, X., Saiyin, H., Wan, B., Zhang, Y., Li , J., Wang, Y., Gao, R., Wang, Y., Dong, W., Najjar, S., Zhang, C., Ding, H., Liu, J., Yu, L. (2012). Brain-selective kinase 2 (BRSK2) phosphorylation on PCTAIRE1 negatively regulates glucose-stimulated insulin secretion in pancreatic β-cells.. 36. The Journal of biological chemistry; 287: 30368-75.
• Liu, J., Colvin, R. (2012). Preface. S-2. BMC Bioinformatics; 13: I1. http://dx.doi.org/10.1186/1471-2105-13-S2-I1.
• Gupta, S., Yan, Y., Malhotra, D., Liu, J., Xie, Z., Najjar, S., Shapiro, J. (2012). Ouabain and insulin induce sodium pump endocytosis in renal epithelium.. 3. Hypertension (Dallas, Tex. : 1979); 59: 665-72.
• 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: http://www.ceser.in/ceserp/index.php/ijts/article/view/145.
• 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. http://www.ncbi.nlm.nih.gov/pubmed/18768112.
• 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. https://dl.acm.org/citation.cfm?id=1746640.1746706.
• 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. http://pubs.acs.org/doi/abs/10.1021/ac0486286.
• 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. http://www.springerlink.com/content/abea06g00yvtyqu3/.
• 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. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1009382.

Patent (1)

• 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.

Book, Chapter in Scholarly Book (5)

• Liu, J. (2011). Segmentation-Assisted Registration for Brain MR Images. Springer Science ; http://www.springer.com/us/book/9781441982032.
• 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 Proceeding (69)

• Nagura, D., Bihl, T., Liu, J. (2024). Boosting Race Car Performance Through Reinforcement Learning from AI Feedback (RLAIF). Dayton: 2024 IEEE National Aerospace and Electronics Conference (NAECON’24).
• Qin, X., Song, S., Brengman, J., Bartone, C., Liu, J. (2024). Robust FOD Detection using Frame Sequence-based DEtection TRansformer (DETR). Singapore : 2024 IEEE Conference on Artificial Intelligence (CAI’24).
• Bihl, T., Farr, P., Liu, J. (2024). Exploring spiking neural networks (SNN) for low Size, Weight, and Power (SWaP) benefits.. Hawaii: Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS’24).
• Song, S., Qin, X., Brengman, J., Bartone, C., Liu, J. (2023). Holistic FOD Detection Via Surface Map and Yolo Networks. 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP); 1-6. https://ieeexplore-ieee-org.proxy.library.ohio.edu/document/10285910 .
• Zhang, Y., Liu, J. (2023). Vertex-based Networks to Accelerate Path Planning Algorithms. . 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP); 1-6.
• Yue, Y., Baltes, M., Abuhajar, N., Smith, C., Bihl, T., Liu, J. (2023). Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation. Athens: IEEE International Symposium on Biomedical Imaging (ISBI'23); http://biomedicalimaging.org/2019/.
• Baltes, M., Smith, C., Liu, J. (2023). Joint ANN-SNN Co-training for Object Localization and Image Segmentation. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'23).
• Liao, B., Chen, Y., Wang, Z., Smith, C., Liu, J. (2022). A Comparative Study on 1.5T - 3T MRI Conversion through Deep Neural Network Models. IEEE International Conference on Machine Learning and Applications (ICMLA'22).
• Sun, T., Gong, S., Wang, Z., Smith, C., Wang, X., Xu, L., Liu, J., Abuhajar, N. (2022). Individualized Conditioning and Negative Distances for Speaker Separation. IEEE International Conference on Machine Learning and Applications (ICMLA'22).
• Song, S., Saunders, K., Yue, Y., Liu, J. (2022). Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning. IEEE International Conference on Machine Learning and Applications (ICMLA'22).
• Sun, T., Gong, S., Wang, Z., Smith, C., Wang, X., Xu, L., Liu, J. (2021). Boosting the Intelligibility of Waveform Speech Enhancement Networks through Self-supervised Representations. IEEE International Conference on Machine Learning and Applications (ICMLA'21).
• Smith, C., McGee, G., Gogineni, S., Bergin, J., Liu, J. (2021). Evaluation of Spiking Neural Networks in Radar. 2021 IEEE National Aerospace & Electronics Conference.
• McGee, G., Smith, C., Gogineni, S., Bergin, J., Liu, J. (2021). Network Fusion for Radar Emitter Detection. 2021 IEEE National Aerospace & Electronics Conference.
• Abuhajar, N., Sun, T., Wang, Z., Gong, S., Smith, C., Wang, X., Xu, L., Liu, J. (2021). Network Compression and Frame Stitching for Efficient and Robust Speech Enhancement. 2021 IEEE National Aerospace & Electronics Conference.
• Song, S., Zhang, Y., Qin, X., Saunders, K., Liu, J. (2021). Vision-based Collision Avoidance through Deep Reinforcement Learning. Athens: 2021 IEEE National Aerospace & Electronics Conference.
• Gong, S., Wang, Z., Sun, T., Smith, C., Xu, L., Liu, J. (2019). Dilated FCN: Listening Longer to Hear Better. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA'19) ; http://www.waspaa.com/.
• Wang, Z., Cai, W., Liu, J. (2019). Automatic Segmentation of Thyroid Colloid and Follicular Cells through QuPath Scripting. STP 38th Annual Symposium Environmental Toxicologic Pathology and One Health; https://www.toxpath.org/AM2019/.
• Wang, Z., Cai, W., Rudmann, D., Liu, J., Rosol, T. (2019). Automatic Segmentation of Thyroid Colloid and Follicular Cells through QuPath Srcripting. STP 38th Annual Symposium Environmental Toxicologic Pathology and One Health; https://www.toxpath.org/AM2019/.
• Wang, Z., Cai, W., Smith, C., Kantake, N., Rosol, T., Liu, J. (2019). Residual Pyramid FCN for Robust Follicle Segmentation. 2019 IEEE International Symposium on Biomedical Imaging (ISBI 2019); http://biomedicalimaging.org/2019/.
• Wang, Z., Cai, W., Kantake, N., Liu, J., Rosol, T. (2018). Neural Networks and Deep Learning to Develop Algorithms for Automated Image Analysis of Thyroid Hypertrophy. 2018 ACVP Annual Meeting; https://www.acvp.org/page/2018Meeting.
• Wang, Z., Cai, W., Smith, C., Kantake, N., Rosol, T., Liu, J. (2018). Residual Pyramid FCN for Robust Follicle Segmentation. 2019 IEEE International Symposium on Biomedical Imaging (ISBI 2019); http://biomedicalimaging.org/2019/.
• Wang, Z., Smith, C., Liu, J. (2018). Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation. 2018 International Conference on Machine Learning in Medical Imaging; http://link.springer.com/chapter/10.1007/978-3-030-00919-9_26.
• Wang, Z., Shi, B., Smith, C., Liu, J. (2018). Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups. International Conference on Pattern Recognition (ICPR'2018); http://ieeexplore.ieee.org/document/8545287.
• Chen, Y., Shi, B., Zhang, P., Smith, C., Liu, J. (2018). Multi-modal Feature Fusion via Deep Networks for AD/MCI Diagnosis. 2018 IEEE International Symposium on Biomedical Imaging (ISBI'2018).
• Chen, Y., Wang, Z., Smith, C., Liu, J. (2018). 3D Brain Tumor Segmentation via Sequential FCN. 2018 IEEE International Symposium on Biomedical Imaging (ISBI'2018).
• Liao, B., Chen, Y., Wang, Z., Smith, C., Liu, J. (2018). Whole-brain 1.5 - 3T MRI Conversion through Multi-view FCN. International Conference on Pattern Recognition (ICPR'2018).
• Chen, Y., Shi, B., Wang, Z., Sun, T., Smith, C., Liu, J. (2017). Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble. Machine Learning on Medical Imaging; https://link.springer.com/chapter/10.1007/978-3-319-67389-9_11.
• Zhang, P., Shi, B., Smith, C., Liu, J. (2017). Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion. Medical Image Computing and Computer Assisted Interventions Conference (MICCAI' 2017); http://www.springer.com/cda/content/document/cda_downloaddocument/9783319661780-c2.pdf?SGWID=0-0-45-1615912-p181094091.
• Chen, Y., Shi, B., Zhang, P., Smith, C., Wang, Z., Liu, J. (2017). Hippocampus Segmentation through Multi-view Ensemble ConvNets. 2017 IEEE International Symposium on Biomedical Imaging (ISBI 2017); http://ieeexplore.ieee.org/document/7950499/.
• Zhang, P., Shi, B., Smith, C., Liu, J. (2016). Nonlinear Metric Learning for Semi-Supervised Learning via Coherent Point Drifting . IEEE International Conference on Machine Learning and Applications (ICMLA'2016); http://ieeexplore.ieee.org/document/7838162/.
• Hobbs, K., Zhang, P., Shi, B., Smith, C., Liu, J. (2016). Quad-mesh Based Radial Distance Biomarkers for Alzheimer's Disease,. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI'2016); 19-23. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7493201.
• Chen, Y., Shi, B., Smith, C., Liu, J. (2015). Nonlinear Feature Transformation and Deep Fusion for Alzheimer’s Disease Staging Analysis. LNCS . Switzerland: Machine Learning in Medical Imaging (MLMI'2015); 9352: pp. 304-312. http:://link.springer.com/chapter/10.1007%2F978-3-319-24888-2_37.
• Shi, B., Chen, Y., Hobbs, K., Smith, C., Liu, J. (2015). Nonlinear Metric Learning for Alzheimer's Disease Diagnosis with Integration of Longitudinal Neuroimaging Features. 1-901725-53-7. British Machine Vision Conference (BMVC'2015); http://www.bmva.org/bmvc/2015/papers/paper138/index.html.
• Shi, B., Wang, Z., Liu, J. (2014). Distance-informed metric learning for Alzheimer’s Disease Staging. 2014 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'2014); http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6943745.
• Hobbs, K., Zhang, P., Liu, J. (2014). Inherent Radial Distances for Robust Hippocampal Atrophy Estimation in Alzheimer’s Disease. 2014 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'2014).
• Shi, B., Liu, J., Berryman, D., List, E., Kelder, B., Kopchick, J. (2013). Development of a whole-body-mouse statistical shape atlas for obesity research. 2013 BMES Annual Meeting.
• Xu, H., Zhang, P., Liu, J. (2013). Towards the Identification of Shape Biomarker(s) for Alzheimer's Disease (AD) based on a Spectral Shape Analysis Framework. 2013 BMES Annual Meeting; http://bmes.org/meetings.
• Shi, B., Liu, J., Xie, S., Berryman, D., List, E. (2013). Robust Separation of Visceral and Subcutaneous Adipose Tissues in Micro-CT of Mice. The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'2013); http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6610000.
• Xu, H., Liu, J. (2013). Spatial-awareness Spectral Embedding (SASE) for Robust Shape Matching. International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2013) ; http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6638019.
• Xie, S., Liu, J., Smith, C. (2012). Riemannian Shape Analysis Based on Meridian Curves. 1. IEEE 11th International Conference on Machine Learning and Applications (ICMLA'2012); 1: 532-537. http://ieeexplore.ieee.org/document/6406618/.
• Xie, S., Liu, J., Smith, C. (2012). Curve Skeleton-based Shape Representation and Classification. International Conference on Image Processing (ICIP) 2012; http://icip2012.com/Papers/PublicSessionIndex3.asp?Sessionid=1204.
• Xu, H., Liu, J., Smith, C. (2012). Robust and efficient point registration based on clusters and Generalized Radial Basis Functions (C-GRBF). international conference on image processing (ICIP'2012); http://ieeexplore.ieee.org/document/6467198/.
• Zhang, W., Liu, J., Liu, Z. (2012). Adaptive re-transmission scheme for wireless mobile networking and computing. 2012 International Conference on Systems and Informatics (ICSAI'2012); 56 - 62 . http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6223067.
• Zhang, W., Liu, J., Liu, Z. (2012). Adaptive re-transmission scheme for wireless mobile networking and computing.. Qingdao: Systems and Informatics (ICSAI), 2012 International Conference on; http://ieeexplore.ieee.org/document/6223067/.
• Xie, S., Liu, J., Smith, C. (2012). A New Shape Analysis Framework based on Curve Skeletons. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'2012); 701-704. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6268628.
• Shi, B., Liu, J. (2012). Regularity-guaranteed transformation estimation in medical image registration. Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141W; 8314: http://proceedings.spiedigitallibrary.org/mobile/proceeding.aspx?articleid=1345935.
• Shi, B., Liu, J. (2011). Non-Twist Regularization for Deformation Estimation. London: Medical Image Analysis and Understanding (MIAU'2011); 151-156. http://www.bmva.org/_media/miua:miua2011proceedings.pdf.
• Mourning, C., Nykl, S., Xu, H., Chelberg, D., Liu, J. (2010). GPU acceleration of robust point matching. 417--426.
• Nykl, S., Mourning, C., xu, H., Chelberg, D., Liu, J. (2010). Lecture Notes in Computer Science 6455, Advances in Visual Computing, Chapter Title: GPU Acceleration of Robust Point Matching. Advances in Visual Computing. Berlin Heidelberg: Springer-Verlag; 6455: 417-426. http://www.springerlink.com/content/c2g8n75p68njq875/.
• Liu, J., Smith, C., Chebrolu, H. (2009). Automatic Multiple Sclerosis detection based on integrated square estimation. Computer Vision and Pattern Recognition Workshops (CVPRW 2009); http://ieeexplore.ieee.org/iel5/5191364/5204041/05204351.pdf.
• Xie, S., Liu, J., Berryman, D., List, E., Smith, C., Chebrolu, H. (2007). A Robust Image Segmentation Model Based on Integrated Square Estimation. International Symposium on Visual Computing (ISVC'2007); https://link.springer.com/chapter/10.1007/978-3-540-76856-2_63.
• Liu, J., Smith, C., Chebrolu, H. (2007). Automatic subcortical structure segmentation using probabilistic atlas. International Symposium on Visual Computing (ISVC'2007); https://link.springer.com/chapter/10.1007/978-3-540-76858-6_17.
• Liu, J., Smith, C., Chebrolu, H. (2007). Automatic Subcortical Structure Segmentation using Local Likelihood-based Active Contour. 3D Segmentation in The Clinic: A Grand Challenge 2007; pp. 91-98. http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p91.pdf.
• Liu, J., Chelberg, D., Chebrolu, H., Smith, C. (2007). Distribution-based Level Set Segmentation for Brain MR Images. Proceedings of the British Machine Vision Conference; http://www.bmva.org/bmvc/2007/papers/paper-222.html.
• Liu, J. (2007). A Local Probabilistic Prior-Based Active Contour Model for Brain MR Image Segmentation. Asian Conference on Computer Vision (ACCV'2007); pp 956-964. https://link.springer.com/chapter/10.1007/978-3-540-76856-2_63.
• Liu, J., Wang, Y., Liu, J. (2006). A Unified Framework for Segmentation-Assisted Image Registration. Asian Conference on Computer Vision (ACCV 2006); pp 405-414. https://link.springer.com/chapter/10.1007/11612704_41.
• Liu, J. (2006). Robust Image Segmentation using Local Median. Computer and Robot Vision, 2006. The 3rd Canadian Conference on; http://ieeexplore.ieee.org/document/1640386/.
• Li, C., Liu, J., Fox, M. (2005). Segmentation of edge preserving gradient vector flow: an approach toward automatically initializing and splitting of snakes. Computer Vision and Pattern Recognition (CVPR'2005); http://ieeexplore.ieee.org/document/1467263/.
• Liu, J. (2005). Segmentation guided registration for medical images. SPIE Medical Imaging; http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1282082.
• Wang, Y., Liu, J. (2005). Segmentation Guided Robust Multimodal Image Registration Using Local Correlation. Annual International Conference of the IEEE ESBS (ESBC'05); https://www.ncbi.nlm.nih.gov/pubmed/17282886.
• Liu, J. (2005). Vector-Valued Local Frequency Representation for Robust Multimodal Image Registration. Annual International Conference of the IEEE ESBS (ESBC'05); https://www.ncbi.nlm.nih.gov/pubmed/17281895.
• Liu, J., Wei, M., Liu, J. (2004). Artifact reduction in mutual-information-based CT-MR image registration. Proceedings of SPIE Medical Imaging; https://www.spiedigitallibrary.org/conference-proceedings-of-spie/5370/0000/Artifact-reduction-in-mutual-information-based-CT-MR-image-registration/10.1117/12.536349.short.
• Yang, L., Welch, L., Liu, J., Cavanaugh, C. (2003). A Robust QoS Forecasting Technique For Dynamic Distributed Real-Time Testbed. New Orleans, LA: IEEE CAMP 2003 International Workshop on Computer Architectures for Machine Perception.
• Liu, J., Liu, J. (2003). Artifacts reduction in mutual information-based image registration using prior information. international conference on image processing 2003; http://ieeexplore.ieee.org/document/1247162/.
• Yang, L., Liu, J., Cavanaugh, C., Welch, L. (2003). A L2E-Based QoS Forecasting Algorithm for a Dynamic, Distributed Real-Time Systems. Las Vegas, NV: The 2003 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'03); 424-429.
• Liu, J., Vemuri, B. (2001). Fast Non-rigid Multimodal Image Registration Using Local Frequency Maps. 1-901725-53-7. Medical Image Computing and Computer Assisted Interventions Conference (MICCAI'2001).
• Liu, J. (2001). Regularized Quadrature Filters for Local Frequency Estimation: Application to Multimodal Volume Image Registration. Vision Modeling and Visualization Conference 2001 (VMV-01); http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.5318&rep=rep1&type=pdf.
• Liu, J., Vemuri, B., Marroquin, J. (2001). Robust Multimodal Image Registration Using Local Frequency Representations. 1-901725-53-7. Information Processing in Medical Imaging (IPMI'01); http://link.springer.com/chapter/10.1007/3-540-45729-1_17.
• Liu, J., Vemuri, B., Bova, F. (2000). Multimodal image registration using local frequency. 1-901725-53-7. Workshop of Computer Vision and Applications (WACV'00); http://ieeexplore.ieee.org/document/895412/.

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; http://www.bmes.org/files/Annual%20Meeting%20Program%20Guides/2012AnnualMeetingProgram.pdf.
• 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; https://www.iscb.org/media-glbio2011.
• Liu, J., Smith, C., Chebrolu, H. (2011). Automatic Multiple Sclerosis detection Based on integrated Square Estimation. Great Lakes Bioinformatics Conference 2011; https://www.iscb.org/media-glbio2011.
• Liu, J., Shi, B. (2011). Regularity Guaranteed Deformation Estimation in Image Registration. Great Lakes Bioinformatics Conference 2011; https://www.iscb.org/media-glbio2011.

Online Article (1)

• Sun, T., Wang, Z., Wang, Z., Smith, C., Liu, J. (2020). TraceCaps: A Capsule-based Neural Network for Semantic Segmentation. ArXiv: Computer Vision and Pattern Recognition; https://arxiv.org/pdf/1901.02920.pdf.

Technical Report (1)

• Shi, B., Liu, J. (2015). Nonlinear Metric Learning for kNN and SVMs through Geometric Transformations. Computing Research Repository; http://arxiv.org/abs/1508.01534.