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Journal Article

  1. [WANG-17]“Application of a LBGK-Based 4D Image Segmentation Method to a 4D-CTA Acquisition of an Intracranial Aneurysm Phantom”, Computing in Science and Engineering, vol. 19, In Press.
  2. [Kong-17]“Cardiac Fiber Tracking Using Adaptive Sampling Based on Diffusion Fractional Anisotropy”, Journal of Medical Imaging and Health Informatics, vol. 7, pp. 1561-1565, 2017.
  3. [Luo-17]“Fast single image super-resolution using estimated low-frequency k-space data in MRI.”, Magn Reson Imaging, vol. 40, pp. 1-11, 2017.
  4. [Zhan-17]“A robust coherent point drift approach based on rotation invariant shape context”, Neurocomputing, vol. 219, pp. 455-473, 2017.
  5. [Kuai-17]“Effect of multiple perfusion components on pseudo-diffusion coefficient in intravoxel incoherent motion imaging.”, Phys Med Biol, vol. 62, pp. 8197-8209, 2017.
  6. [ROBI-17]“Global Diffusion Tractography by Simulated Annealing”, IEEE Transactions on Biomedical Engineering, vol. 64, pp. 649-660, 2017.
  7. [LUO-17]“A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data.”, Med Biol Eng Comput, 2017.
  8. [WANG-16]“Multilevel segmentation of intracranial aneurysms in CT angiography images”, Medical Physics, vol. 43, pp. 1777 - 1786, 2016.
  9. [Huan-16]“Cardiac Diffusion Tensor Imaging Based on Compressed Sensing Using Joint Sparsity and Low-Rank Approximation”, Technology and Health Care, vol. 24, pp. 593–599, 2016.
  10. [kuai-16]“Generalization of Intravoxel Incoherent Motion Model by Introducing the Notion of Continuous Pseudo-diffusion Variable”, Magnetic Resonance in Medicine, vol. 76, pp. 1594-1603, 2016.
  11. [Zhan-16]“Automatic tracking of neural stem cells in sequential digital images”, vol. 36, pp. 66–75, 2016.
  12. [WANG-16]“Image-based investigation of human in vivo myofibre strain”, IEEE Trans Med Imaging, vol. 35, pp. 2486-2496, 2016.
  13. [Kong-16a]“Cardiac fiber tracking using adaptive particle filtering based on tensor rotation invariant in MRI.”, Phys Med Biol, vol. 61, pp. 1888-903, 2016.
  14. [Kong-16b]“Cardiac fiber tracking using adaptive particle filtering based on tensor rotation invariant in MRI”, Physics in Medicine & Biology, vol. 61(5), pp. 1888-1903, 2016.
  15. [Sun-16]“Assessment of the characteristics of orientation distribution functions in hardi using morphological metrics”, PLOS ONE, 2016.
  16. [Wang-15]R. Wang, Li, C., Wang, J., Wei, X. E., Li, Y. H., Hui, C., Zhu, Y. M., and Zhang, S., “Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images”, Journal of Neuroscience Methods, vol. 241, pp. 30-36, 2015.
  17. [Robi-15]M. Robini and Zhu, Y. M., “Generic Half-Quadratic Optimization for Image Reconstruction”, SIAM J. Imaging Sci., vol. 8, pp. 1752-1797, 2015.
  18. [LI-15]H. Li, Robini, M., Magnin, I. E., and Zhu, Y. M., “Cardiac fiber unfolding by semidefinite programming”, IEEE Trans. on Biomedical Engineering, vol. 62, 2015.
  19. [chu2015estimating]“Estimating Intravoxel Fiber Architecture Using Constrained Compressed Sensing Combined with Multitensor Adaptive Smoothing”, International Journal of Imaging Systems and Technology, vol. 25, pp. 285–296, 2015.
  20. [Song-15]Y. P. Song, Yang, J., Qiao, Y., and Zhu, Y. M., “Volume visualization based on the intensity and SUSAN transfer function spaces”, Biomedical Signal Processing and Control, vol. 18, pp. 110-117, 2015.
  21. [chu2015multifiber]“Multifiber pathway reconstruction using bundle constrained streamline”, Computerized Medical Imaging and Graphics, vol. 46, pp. 291–299, 2015.
  22. [Kong-15]“A New Cardiac Fiber Tracking Method Using Particle Filtering in MRI”, Journal of Medical Imaging and Health Informatics, vol. 5, pp. 1438 - 1443, 2015.
    http://www.ingentaconnect.com/content/10.1166/jmihi.2015.1551http://www.ingentaconnect.com/content/asp/jmihi/2015/00000005/00000007/art00010http://www.ingentaconnect.com/content/asp/jmihi/2015/00000005/00000007/art00010
  23. [Wang-15]R. Wang, Li, C., Wang, J., Wei, X. E., Li, Y. H., Hui, C., Zhu, Y. M., and Zhang, S., “Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution”, Neuroradiology, vol. 57, pp. 307–320, 2015.
  24. [WEI-15]H. Wei, Viallon, M., Delattre, B., Moulin, K., Yang, F., Croisille, P., and Zhu, Y. M., “Free-Breathing Diffusion Tensor Imaging and Tractography of the Human Heart in Healthy Volunteers Using Wavelet-Based Image Fusion.”, IEEE Trans Med Imaging, vol. 34, pp. 306-316, 2015.
  25. [sun2015quantitative]“Quantitative representation and description of intravoxel fiber complexity in HARDI”, Physics in medicine and biology, vol. 60, p. 8417, 2015.
  26. [Song-15]Y. P. Song, Yang, J., Zhou, L., and Zhu, Y. M., “Electric Field Based Transfer Functions for Volume Visualization”, Journal of Medical and Biological Engineering, 2015.
  27. [Chu-15]C. Y. Chu, Huang, J. P., SUN, C. Y., Liu, W. Y., and Zhu, Y. M., “Resolving Intravoxel Fiber Architecture Using Nonconvex Regularized Blind Compressed Sensing”, Physics in Medicine and Biology, vol. 60, pp. 2339–2354, 2015.
  28. [Wang-14]R. Wang, Li, C., Wang, J., Wei, X. E., Li, Y. H., Hui, C., Zhu, Y. M., and Zhang, S., “Automatic Segmentation and Quantitative Analysis of White Matter Hyperintensities on FLAIR Images Using Trimmed-Likelihood Estimator”, Academic Radiology, vol. 21, pp. 1512–1523, 2014.
  29. [Wang-14]R. Wang, Li, C., Wang, J., Wei, X. E., Li, Y. H., Hui, C., Zhu, Y. M., and Zhang, S., “Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy”, Magnetic Resonance Imaging, vol. 32, pp. 1321–1329, 2014.
  30. [YANG-14]F. Yang, Zhu, Y. M., Luo, J. H., Robini, M., Liu, J., and Croisille, P., “A comparative study of different level interpolations for improving spatial resolution in diffusion tensor imaging”, IEEE Journal on Biomedical and Health Informatics, vol. 18, pp. 1317-1327, 2014.
  31. [WEI-13]H. Wei, Viallon, M., Delattre, B., Wang, L., Pai, V., Wen, H., Xue, H., Guetter, C., Croisille, P., and Zhu, Y. M., “Assessment of cardiac motion effects on the fiber architecture of the human heart in vivo.”, IEEE transactions on medical imaging, 2013.
  32. [Wei-13]H. J. Wei, Viallon, M., Delattre, B. M. A., Wang, L. H., Pai, V. M., Wen, H., Xue, H., Guetter, C., Croisille, P., and Zhu, Y. M., “Assessment of cardiac motion effects on the fiber architecture of the human heart in vivo”, IEEE Trans. on Medical Imaging, vol. 32, pp. 1928-1938, 2013.
  33. [LIU-13]Q. Liu, Wang, S., Yang, K., Luo, J., Zhu, Y. M., and Liang, D., “Highly undersampled magnetic resonance image reconstruction using two-level bregman method with dictionary updating.”, IEEE transactions on medical imaging, vol. 32, no. 7, pp. 1290-301, 2013.
  34. [ROBI-13b]M. Robini, Zhu, Y. M., and Luo, J. H., “Edge-preserving reconstruction with contour-line smoothing and non-quadratic data-fidelity”, Inverse Problems and Imaging, vol. 7, pp. 1331-1366, 2013.
  35. [Liu-13]Q. G. Liu, Wang, S. S., Yang, K., Luo, J. H., Zhu, Y. M., and Liang, D., “Highly undersampled magnetic resonance image reconstruction using two-level Bregman method with dictionary updating”, IEEE Trans. on Medical Imaging, vol. 32, pp. 1290-1301, 2013.
  36. [ZHANG-13]Y. L. Zhang, Liu, W. Y., Magnin, I. E., and Zhu, Y. M., “Feature-Preserving Smoothing of Diffusion Weighted Images Using Nonstationarity Adaptive Filtering”, IEEE Trans. on Biomedical Engineering, vol. 60, pp. 1693-1701, 2013.
  37. [Xin-13]H. M. Xin and Zhu, Y. M., “Accurate non-linear registration for two-dimensional gel electrophoresis images”, Journal of Biological Systems, vol. 21, 2013.
  38. [BAO-13]L. Bao, Robini, M., Liu, W. Y., and Zhu, Y. M., “Structure-adaptive sparse denoising for diffusion-tensor MRI”, Medical Image Analysis, vol. 17, pp. 442-457, 2013.
  39. [Liu-13]Q. G. Liu, Liang, D., Song, Y., Luo, J. H., Zhu, Y. M., and Li, W. S., “Augmented Lagrangian-Based Sparse Representation Method with Dictionary Updating for Image Deblurring”, SIAM J. Imaging Sci, vol. 6, pp. 1689–1718, 2013.
  40. [LUO-12]J. H. Luo and Zhu, Y. M., “Denoising of Medical Images Using a Reconstruction-Average Mechanism”, Digital Signal Processing, vol. 22, pp. 337–347, 2012.
  41. [Wang-12]S. S. Wang, Xia, Y., Liu, Q. G., Luo, J. H., Zhu, Y. M., and Feng, D. D., “Gabor Feature Based Nonlocal Means Filter for Textured Image Denoising”, Journal of Visual Communication and Image Representation, vol. 23, pp. 1008–1018, 2012.
  42. [WANG-12]L. H. Wang, Zhu, Y. M., Li, H. Y., Liu, W. Y., and Magnin, I. E., “Multi-Scale Modeling and Simulation of the Cardiac Fiber Architecture for DMRI”, IEEE Trans. Biomedical Engineering, vol. 59, pp. 16-19, 2012.
  43. [LIAO-12]K. Liao, Zhu, Y. M., Ding, L., Valette, S., Zhang, W., and Dickens, D., “Sparse imaging of cortical electrical current densities via wavelet transforms”, Physics in Medicine and Biology, vol. 57, p. 6881--6901, 2012.
  44. [LUO-12b]J. H. Luo, Wang, S. S., Li, W. Q., and Zhu, Y. M., “Removal of truncation artefacts in magnetic resonance images by recovering missing spectral data”, Journal of Magnetic Resonance, vol. 224, pp. 82-93, 2012.
  45. [LUO-12]J. H. Luo, Zhu, Y. M., Li, W., Croisille, P., and Magnin, I. E., “MRI reconstruction from 2D truncated k-space.”, Journal of magnetic resonance imaging : JMRI, vol. 35, no. 5, pp. 1196-206, 2012.
  46. [YANG-12]F. Yang, Zhu, Y. M., Magnin, I. E., Luo, J. H., Croisille, P., and Kingsley, P. B., “Feature-Based Interpolation of Diffusion Tensor Fields and Application to Human Cardiac DT-MRI”, Medical Image Analysis, vol. 16, pp. 459-481, 2012.
  47. [DELA-12]B. M. Delattre, Viallon, M., Wei, H. J., Zhu, Y. M., Feiweier, T., Pai, V. M., Wen, H., and Croisille, P., “In-vivo cardiac diffusion weighted MRI: quantification of normal perfusion and diffusion coefficients with intravoxel incoherent motion imaging”, Invest. Radiol, 2012.
  48. [Liu-12]Q. G. Liu, Wang, S. S., Luo, J. H., and Zhu, Y. M., “An augmented Lagrangian approach to general dictionary learning for image denoising”, J. Visual Communication and Image Representation, vol. 5, pp. 753-766, 2012.
  49. [ZHANG-11b]N. Zhang, Liao, Q. M., Ruan, S., Lebonvallet, S., and Zhu, Y. M., “A novel segmentation and evaluation system for brain tumor based on 3D information in MRI image sequences”, Journal of Tsinghua University, vol. 51, pp. 96-100, 2011.
  50. [ZHANG-11]N. Zhang, Ruan, S., Lebonvallet, S., Liao, Q. M., and Zhu, Y. M., “Kernel Feature Selection to Fuse Multi-spectral MRI Images for Brain Tumor Segmentation”, Computer Vision and Image Understanding, vol. 115, pp. 256 - 269, 2011.
  51. [YANG-11]F. Yang, Zhu, Y. M., Rappachi, S., Luo, J. H., Robini, M., and Croisille, P., “Interpolation of vector fields from human cardiac DT-MRI”, Physics in Medicine and Biology, vol. 56, pp. 1415-1430, 2011.
  52. [Song-11]X. Song, Liu, F., Zou, Z. J., Zhu, Y. M., Yin, J. C., and Xu, F., “Nonlinear underwater robot controller design with adaptive disturbance prediction and smoother”, International Journal of Computational Intelligence Systems, vol. 4, pp. 634-643, 2011.
  53. [LUO-11c]J. H. Luo, Liu, J. H., Li, W. Q., Zhu, Y. M., and Jiang, R. Y., “Image Reconstruction from Sparse Projections Using S-Transform,”, Journal of Mathematical Imaging and Vision, vol. 10, pp. 1-13, 2011.
  54. [Xie-11]K. Xie, Yang, S., and Zhu, Y. M., “3D visualization and simulation in surgical planning system of orbital hypertelorism”, Journal of Medical Systems, vol. 35, pp. 617-623, 2011.
  55. [LIU-11]Q. G. Liu, Luo, J. H., and Zhu, Y. M., “Adaptive Image Decomposition by Improved Bilateral Filter”, International Journal of Computer Applications, vol. 23, pp. 16-22, 2011.
  56. [WANG-11]Y. Wang, Zhu, Y. M., Courbebaisse, G., and Diing, L., “Detection and Extraction of Feature Points in Robot Vision Using Lattice Boltzmann Method”, Science Technology and Engineering, Chinese National Knowledge Infrastructure (CNKI), vol. 11, pp. 8045-8048, 2011.
  57. [BERK-11]M. Berkane, Clarysse, P., Njiwa, J. Y., Zhu, Y. M., and Magnin, I. E., “A neural network based summarizing method of periodic image sequences”, Neural Network World, vol. 20, pp. 687-703, 2011.
  58. [LUO-10]J. H. Luo, Li, W. Q., and Zhu, Y. M., “Image Reconstruction from Limited-Angle Projections Based on -u Spectrum Analysis”, IEEE Trans. on Image Processing, vol. 19, pp. 131-140, 2010.
  59. [Berk-10]M. Berkane, Clarysse, P., Njiwa, J. Y., Zhu, Y. M., and Magnin, I. E., “A neural network based summarizing method of periodic image sequences”, Neural Network World, vol. 20, pp. 687-703, 2010.
  60. [FRIN-10]C. Frindel, Robini, M., Croisille, P., and Zhu, Y. M., “A graph-based approach for automatic cardiac tractography”, Magn Reson Med, vol. 64, pp. 1215-1229, 2010.
  61. [LUO-10]J. H. Luo, Zhu, Y. M., and Hiba, B., “Medical Image Denoising Using One-Dimensional Singularity Function Model”, Computerized Medical Imaging and Graphics, vol. 34, pp. 167-176, 2010.
  62. [NJIWA-10]J. Y. Njiwa, Zhu, Y. M., Luo, J. H., and Hiba, B., “Reconstruction of Cardiac Cine MR Images from Partial k-Space”, SRX Physics, p. doi:10.3814/2010/850392, 2010.
  63. [XIE-10]K. Xie and Zhu, Y. M., “Interactive Surgery Simulation for the Nose Augmentation Using CT Data”, Neural Computing & Applications, vol. 19, pp. 61-65, 2010.
  64. [QIN-09]H. X. Qin, Yang, J., and Zhu, Y. M., “A Feature-Preserving Filtering Approach for Point Set Surface and Surface Attributes Based on Robust Statistics”, J Inf Sci Eng, vol. 25, pp. 1135-1146, 2009.
  65. [SHI-09]F. Shi, Yang, J., and Zhu, Y. M., “Automatic segmentation of bladder in CT images”, Journal of Zhejiang University: Science A, vol. 10, pp. 239-246, 2009.
  66. [FRIN-09]C. Frindel, Robini, M., Croisille, P., and Zhu, Y. M., “Comparison of regularization methods for human cardiac diffusion tensor MRI”, Med Image Anal, vol. 13, pp. 405–418, 2009.
  67. [LUO-09b]J. H. Luo and Zhu, Y. M., “Image Reconstruction Scheme Based on Phase Correction and Singularity Function Analysis Model”, J Signal Proces Syst, vol. 54, pp. 79–88, 2009.
  68. [XIE-09a]K. Xie and Zhu, Y. M., “Interactive Surgery Simulation for the Nose Augmentation Using CT Data”, Neural Comput Appl, vol. 19, pp. 61-65, 2009.
  69. [LUO-09a]J. H. Luo, Zhu, Y. M., and Magnin, I. E., “Denoising by Averaging Reconstructed Images: Application to Magnetic Resonance Images”, IEEE Trans. on Biomedical Engineering, vol. 56, pp. 666–674, 2009.
  70. [XIN-09]H. M. Xin and Zhu, Y. M., “Multiple information-based spot matching method for two-dimensional gel electrophoresis images”, Electrophoresis, vol. 30, pp. 2477–2480, 2009.
  71. [BAO-09]L. J. Bao, Zhu, Y. M., Liu, W. Y., Croisille, P., Pu, Z. B., Robini, M., and Magnin, I. E., “Denoising of human cardiac diffusion tensor magnetic resonance images using sparse representation combined with segmentation”, Phys Med Biol, vol. 54, pp. 1435–1456, 2009.
  72. [LUO-09]J. H. Luo, Luo, H., and Zhu, Y. M., “Image Reconstruction Scheme Based on Phase Correction and Singularity Function Analysis Model”, Journal of Signal Processing Systems, vol. 54, pp. 79-88, 2009.
  73. [XIE-08a]K. Xie, Yang, J., and Zhu, Y. M., “Real-time visualization of large volume datasets on standard PC hardware”, Comput Meth Prog Bio, vol. 90, pp. 117–123, 2008.
  74. [XIE-08b]K. Xie, Yang, J., and Zhu, Y. M., “The 3D Cutting Algorithm Based on Surface Reconstruction”, Sciencepaper Online, vol. June, 2008.
  75. [XIE-08c]K. Xie, Yang, S., and Zhu, Y. M., “High-Quality Multi-resolution Volume Rendering Based on Medical Dataset”, Sciencepaper Online, vol. June, 2008.
  76. [LUO-08]J. H. Luo, Zhu, Y. M., and Magnin, I. E., “Phase correction based singularity function analysis for partial k-space reconstruction”, Magn Reson Imaging, vol. 26, pp. (746–753), 2008.
    http://linkinghub.elsevier.com/retrieve/pii/S0730725X08000581
  77. [LIU-08]W. Y. Liu, Tang, Z. J., Zhu, Y. M., Hu, S., Zhang, Y. L., Wang, L. H., and Magnin, I. E., “Novel data enhancement technology for ultrasonic stereovision”, Optics and Precision Engineering, vol. 16, pp. 1759–1766, 2008.
  78. [QIN-08a]H. X. Qin, Yang, J., and Zhu, Y. M., “A Novel Meshless Method for Point Set Surface Processing”, Optical Engineering, vol. 47, 2008.
  79. [QIN-08b]H. X. Qin, Yang, J., and Zhu, Y. M., “Nonuniform Bilateral filtering for Point Sets and Surface Attributes”, The Visual Computer-International Journal of Computer Graphics, vol. 24, pp. 1067–1074, 2008.
  80. [MILL-07]J. Milles, Zhu, Y. M., Gimenez, G., Guttmann, C. R. G., and Magnin, I. E., “MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information”, Comput Med Imag Graph, vol. 31, pp. 81–90, 2007.
    http://www.ncbi.nlm.nih.gov/pubmed/17196790
  81. [NJIWA-07]J. Y. Njiwa, Zhu, Y. M., Robini, M., and Magnin, I. E., “Magnetic Resonance Image Reconstruction Using Analytic Image Representation”, Nuclear Instruments and Methods in Physics Research A, vol. 571, pp. 73-76, 2007.
  82. [MIAO-07]P. Miao, Hiba, B., Luo, J. H., Sappey-Marinier, D., Magnin, I. E., and Zhu, Y. M., “MR image reconstruction from partial k-space using singularity function representations”, Comput Med Imag Graph, vol. 571, pp. 239–242, 2007.
  83. [XIE-07a]K. Xie, Yang, J., and Zhu, Y. M., “Fast collision detection based on nose augmentation virtual surgery”, Comput Meth Prog Bio, vol. 88, pp. 1–7, 2007.
  84. [XIE-07b]K. Xie, Yang, J., and Zhu, Y. M., “Real time volume rendering of large datasets on standard PC hardware”, Computer Applications, vol. 27, pp. 1129–1131, 2007.
  85. [XIE-07c]K. Xie, Yang, J., and Zhu, Y. M., “Interactive volume cutting of medical data”, Comput Biol Med, vol. 37, pp. 1155–1159, 2007.
  86. [LI-06]X. L. Li, Yang, J., Xie, K., and Zhu, Y. M., “High Quality Rendering with Depth Cueing of Volumetric Data Using Monte Carlo Integration”, Comput Biol Med, vol. 36, pp. 1014–1025, 2006.
  87. [BELA-06]B. Belaroussi, Milles, J., Carme, S., Zhu, Y. M., and Benoit-Cattin, H., “Intensity non-uniformity correction in MRI: Existing methods and their validation”, Med Image Anal, vol. 10, pp. 234-246, 2006.
  88. [YU-06]Z. Q. Yu, Zhu, Y. M., Yang, J., and Zhu, Y. M., “Hybrid Region-Boundary Model for Cerebral Cortical Segmentation in MRI”, Comput Med Imag Grap, vol. 30, pp. 197–208, 2006.
  89. [YU-06]Z. Q. Yu, Zhu, Y. M., Yang, J., and Zhu, Y. M., “Hybrid Region-Boundary Model for Cerebral Cortical Segmentation in MRI”, Comput Med Imag Grap, vol. 30, pp. 197–208, 2006.
  90. [LI-06b]X. L. Li, Yang, J., and Zhu, Y. M., “DRR generation by an adaptive Monte Carlo method”, Phys Med Biol, vol. 51, pp. 2745–2752, 2006.
  91. [BELA-06]B. Belaroussi, J. Milles, C. S., Zhu, Y. M., and Benoit-Cattin, H., “Intensity Nonuniformity Correction in MRI: Existing Methods and Their Validation”, Medical Image Analysis, vol. 10, pp. 234-246, 2006.
  92. [LI-06a]X. L. Li, Yang, J., and Zhu, Y. M., “Combining Estimators for Monte Carlo Volume Rendering with Shading”, Visual Comput, vol. 22, pp. 468–477, 2006.
  93. [MILL-06]J. Milles, Zhu, Y. M., Chen, N., Panych, L., Gimenez, G., and Guttmann, C. R. G., “Computation of Transmitted and Received B1 Fields in Magnetic Resonance Imaging”, IEEE Trans Biomed Engin, vol. 53, pp. 885-895, 2006.
  94. [KAFT-05]V. Kaftandjian, Zhu, Y. M., Dupuis, O., and Babot, D., “The combined use of the evidence theory and fuzzy logic for improving multimodal non destructive testing systems”, IEEE Trans Instr Measur, vol. 54, pp. 1968-1977, 2005.
  95. [LUO-05]J. H. Luo, Zhu, Y. M., Clarysse, P., and Magnin, I. E., “Correction of Bias Field in MR Images Using Singularity Function Analysis”, IEEE Trans Med Imaging, vol. 24, pp. 1067-1085, 2005.
  96. [XIE-05c]K. Xie, Yang, J., and Zhu, Y. M., “Semi-automated brain tumor and edema segmentation using MRI”, Eur J Radiol, vol. 56, pp. 12-19, 2005.
    http://linkinghub.elsevier.com/retrieve/pii/S0720048X05001518
  97. [XIE-05b]K. Xie, Yang, J., and Zhu, Y. M., “Real-time rendering of 3D medical data sets”, Future Generation Computer Systems, vol. 21, pp. 573-581, 2005.
  98. [XIE-05]K. Xie, Yang, J., Zhu, Y. M., and Li, X. L., “HVS-Based medical image compression”, Eur J Radiol, vol. 55, pp. 139-145, 2005.
  99. [ZHAO-05]J. Zhao, Zhu, Y. M., Sing, P. M., Fan, Q., and Luo, J. H., “Recognition of gene acceptor site based on multi-objective optimization”, Acta Biochim Biophys Sin, vol. 37, pp. 435-439, 2005.
  100. [ZHOU-04]L. Q. Zhou, Zhu, Y. M., Grimaud, J., Hermier, M., Rovaris, M., and Filippi, M., “A new method for analyzing histograms of brain magnetization transfer ratios: comparison with existing techniques”, Am J Neuro Radiol, vol. 25, pp. 1234-1241, 2004.

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