YANG
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YANG CHEN

Born in 1979,Doctor/Postdoctor,a professor and doctoral supervisor at the School of Computer Science and Engineering,Southeast University;Imaging Science and Technology Laboratory Executive Deputy Director; IEEE senior member;Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University Executive Deputy Director;IEEE Transactions on Computational Imaging Associate Edito。

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RECORD

  • 2007 - Now, Professor and Doctoral supervisor at School of computer science and engineering, Southeast University.
  • September 2014 - September 2015, worked as a visiting scholar in University of Wisconsin-Madison, USA.
  • March 2011 - March 2013, Postdoctor,Institut National de la Santé et de la Recherche Médicale(INSERM),Doctoral Supervisor:Prof.Jean-Louis COATRIEUX and Christine TOUMOULIN.
  • March 2008 - March 2010, Postdoctor,Chinese University of Hong Kong,Department of Electronic Engineering,Doctoral Supervisor:Prof. Emma MACPHERSON.
  • August 2002 - August 2007, Doctor, First Military Medical University,Biomedical Engineering.
  • August 1996 - June 2001, Bachelor,First Military Medical University,Biomedical Engineering.

    • DETAILS

  • Research Direction:(1) Artificial intelligence algorithms and their applications; (2) Computer Vision; (3) pattern recognition; (4) Medical Image Imaging, Analysis and Processing; (5) Medical Signal Image Processing and Analysis.
  • 先后在《IEEE Transactions on Medical Imaging》、《IEEE Transactions on Image Processing》、《IEEE Transactions on Circuits and Systems for Video Technology》、《IEEE Transactions on Computational Imaging》、《Medical Image Analysis》、《International Journal of Computer Vision》、《International Journal of Computer Vision》、《Optics Express》、《Medical Physics》、《中国生物医学工程学报》、《CT理论与应用研究》、《光学学报》、《电子学报》、《计算机工程与应用》国内外著名期刊和MICCAI、ISBI、SPIE等重要国际会议发表学术论文100余篇，出版专著1部，教材2部，获得授权发明专利10余项、软件著作权2项。论文被SCI/EI收录100多篇次，被他人引用累计超过5000多篇次，其中ESI高被引论文6篇，ESI热点论文1篇，引文作者包括相关领域的权威学者，如美国耶鲁大学转化研究成像学中心主任Albert J. Sinusas教授、英属哥伦比亚大学Rabab K. Ward教授(IEEE Fellow, 加拿大工程院院士，IEEE Signal Processing Society现任主席)、普度大学的Charles A. Bouman教授(IEEE Fellow)、美国密歇根大学Jeffrey A. Fessler 教授(IEEE Fellow)、美国北卡罗来纳州达勒姆杜克大学Sina Farsiu教授（IEEE Fellow, OSA Fellow, SPIE Fellow, AIMBE Fellow）、马萨诸塞州总医院Mannudeep K. Kalra教授（MGH Webster 辐射高级研究和教育中心主任）、德国慕尼黑大学生物工程学院院长Franz Pfeiffer教授、巴黎多菲纳大学Laurent D. Cohen 教授（法国国家科学研究所主任、IEEE Fellow）、特拉维夫大学医学图像处理和分析实验室主任Hayit Greenspan教授等，部分成果被国外同行在引文中称为“first proposed (首次提出)”、“Novel (新颖的)”、“good performance (好的性能)”、“greatly improved (显著提高)”和 “It has been proved to be effective and promising (被证明是非常有效和有前途的)”等。多项科研成果已通过专利形式转化到国内医学影像公司，例如基于区别性特征稀疏表示的正则化重建算法和基于深度卷积神经网络的磁共振图像增强算法已应用于宁波鑫高益公司的磁共振成像设备系统 (SuperScan 和 Opera两种型号)， 2017-2018年累计新增产值约4000万元，新增利润约400万元；基于深度特征学习的图像重建算法目前正在联影医疗公司和宁波鑫高益公司进行测试，预计将在这两家公司的新一代旋转三维DSA，乳腺层析成像(国内首台)，螺旋CT以及磁共振设备上得到应用；基于区别性特征表示的CT图像后处理算法、心血管提取分析，血流计算以及肾功能量化计算等算法软件也在江苏省人民医院、山东省肿瘤医院、东部战区总医院和北部战区总医院等知名三甲医院完成了临床应用测试。

  • 指导学生竞赛：本人非常注重国内外组织的专业竞赛活动，在竞赛实践中锻炼学生的专业技术和解决问题的能力，培养他们对专业学习的自信心和创新能力，提升他们的就业竞争力，同时以培养更优秀的青年及高素质人才为目标，做好学生成长成才的指导者和引路人，且已指导多名博士、硕士研究生以第一作者在本领域top期刊发表论文；先后推荐多名研究生赴世界名校攻读并完成博士学位攻读（如美国耶鲁大学、美国威斯康辛麦迪逊大学、荷兰莱顿大学等）。
  • (1)2014年MICCAI会议冠状动脉自动钙化分数比赛（Coronary Calcium Scoring (orCaScore) challenge），指导学生提出的算法钙化点检测特异性和灵敏度指标在参赛的5支队伍中均排名第一；
  • (2)2016年美国Mayo Clinic组织的Low-dose CT竞赛，指导学生获得Case Scoring和其余四个团队并列第一，Lesion Scoring和另外两只队伍并列第三；
  • (3)2018年第一届深圳医疗健康大数据创新应用国际大赛，指导学生获得创新应用组一等奖和标准组二等奖；
  • (4)2018年江苏省人工智能大赛，指导学生获得三等奖；
  • (5)2021年NTIRE(New Trend in Image Restoration and Enhancement Workshop) CVPR(Computer Vision and Pattern Recognition) 学习超分辨率空间国际大赛，指导学生获得亚军等

  • Postgraduate Career Prospercts: (1) Recommended to Stanford, Yale and other world-renowned Universities for Doctoral Studies; (2) Famous Companies at home and abroad.

  • Talent Demand
    • 1 - 2 Postdocs/ Year; 1 - 2 Doctoral Students/ Year; 2- 10 postgraduates/ Year; 1 - 5 undergraduates /Year.
    Email Address： chenyang.list@seu.edu.cn.

    WORK

  • 提出基于非局部先验信息的断层医学图像重建算法

    • 图1.对全身PET图像采用所提出的非局部先验Bayesian重建算法的冠状面重建结果。从左至右分别是对应的CT图像、采用OSEM算法重建的PET图像以及所提出的非局部先验Bayesian重建算法的PET图像。图中红色箭头所指的是一右肺肿瘤所在位置。

           申请人于2008在国际上首次提出了基于非局部先验的Bayesian断层医学图像重建算法，该算法突破了传统的Bayesian重建算法只利用基于孤立像素梯度信息构建先验信息的限制，充分的利用图像中更多的结构特征和相似特征的冗余信息实现了对PET重建中图像伪影和噪声的有效抑制(见图1)。相关的论文发表于数学成像领域国际主流期刊 Journal of Mathematical Imaging and Vision (2008, 30: 133-146) 。从图1可见所提出的Bayesian重建算法相对于传统的OSEM重建算法能够显著提高PET重建的质量, 提高对PET对高代谢的肿瘤组织的成像质量。随后利用该非局部先验的重建算法显著提高了CT断层图像的重建质量(见图2)。

    图2. (a) 对腹部CT投影数据采用FBP算法的重建图像；(b) 对腹部低剂量数据采用所提出的基于非局部先验的迭代重建算法重建的图像。

    图3.目标能量函数随着迭代次数的变化。

           进一步的，为了实现对非局部先验中的权重参数在每次迭代中的优化估计，我们提出基于块相似性混合先验的联合重建算法，解决了此基于非局部先验的重建算法中多个参数联合估计的收敛性问题(从图3可以看到该联合重建算法能够保证目标能量函数值随着迭代次数的增加而趋于收敛，从而得到稳定的断层图像重建结果)。

  • 提出基于人体组织相似特征区别性的低剂量CT图像后处理算法

    •        目前, CT扫描已经成为国内各级医院影像诊断科室必不可少的常规检查手段，考虑到国内医院(尤其是大中型医院)的巨大门诊量，病人在CT扫描中所受的辐射伤害不可忽视。从二十一世纪初开始，国内的一些科研和企业研发机构就开始了低剂量CT成像方面的工作。长期以来，CT研究领域的研究人员普遍将迭代重建算法看做是低剂量CT成像的解决方法，并提出了一系列迭代重建算法以提高低剂量CT重建的图像质量。与此同时，国内的CT设备制造商也提出了各自的低剂量重建算法，具体包括东软公司的ClearView双域迭代重建技术、联影公司的KARL 3D迭代重建技术以及安科公司的LISATM迭代重建降噪技术。然而，目前国内医院中的绝大部分CT设备均没有内置新型低剂量重建算法，重新购买那些内置了低剂量算法的新型CT设备对大部分中小医院将是一笔巨大的费用。尤其的，迭代类重建算法受限于所需的较大的计算量以及对投影数据获取的依赖，无法直接应用于当前国内临床医院大部分的CT设备。为了给国内大部分的CT设备提供不依赖投影数据的低剂量CT成像解决方案，申请人从对正常CT组织结构和噪声伪影特征的分析入手，利用CT图像中正常衰减组织的相似性特征以及噪声伪影区别性特征设计了一系列低剂量CT图像后处理算法，在有效保持原CT图像中人体组织结构特征的同时实现了对低剂量CT图像中伪影和噪声的有效抑制。

           我们首先提出了基于大尺度CT值平均的低剂量CT图像后处理算法，该算法利用同类人体组织在CT图像中的全局分布特点，在一个较大的搜索窗中利用块的相似性隶属度函数将属于同类组织的像素点引入到对当前像素点的处理之中，通过对低剂量CT图像中的同类组织像素点的直接平均操作实现了对噪声和伪影的有效抑制，实验结果证明该算法能够对扫描管电流(剂量)降低到正常数值四分之一的情况下的低剂量CT腹部图像进行有效的处理，使之提高到正常剂量的腹部CT图像质量，该算法目前已经成功的应用于腹部低剂量CT图像的处理中。随后我们根据胸部低剂量CT图像中的伪影在高频带分布显著的性质，进一步对后处理算法进行改进，在不同的尺度上分别进行伪影去除和噪声抑制，提出小波空间伪影抑制和图像空间噪声抑制相结合的处理算法，进一步提高了低剂量CT图像的处理效果(见图3)。

    图3. 用小波空间伪影抑制和图像空间噪声抑制相结合的处理算法对低剂量CT体数据 (管电流为30mAs) 的处理结果。(a)和(c)为原三维低剂量CT前后冠状面图；(b)和(d)为用基于特征相似性去噪和多尺度伪影抑制的算法处理后的三维低剂量CT前后冠状面图(对应(a)和(c))。

           随后从特征分解的角度设计低剂量CT图像处理算法，首先采用样本采集和特征学习构建多尺度区别性特征字典，然后通过区别性稀疏编码实现对正常组织特征和噪声伪影的分解，临床实验结果表明该算法能够有效的抑制低剂量CT图像中的伪影和噪声，显著提高低剂量CT图像质量，值得一提的是该文中的实验结果表明所提出的后处理算法能够获得比经典的TV迭代重建算法更好的伪影抑制的效果。随后，为了进一步提高算法在临床上的实用性并降低复杂度和参数敏感性，我们在此算法的基础上提出了基于三维区别性特征分解的低剂量CT处理算法，该算法从高剂量的病人图像数据中提取三维人体组织特征并同时从不同剂量的体摸图像数据的差异信息中获得伪影和噪声特征，构建用于稀疏分解的三维区别性特征字典，进一步提高了低剂量CT成像算法的效果，尤其的对比图4中放大的感兴趣区域(d)和(e), 我们可以发现相对于GE公司最新的VEO算法，所提出的特征分解的算法能获得更高的软组织结构对比度。

    图4：采用基于特征分解的算法对低剂量腹部CT图像处理的结果。(a) FBP重建结果； (b) 基于区别性特征分解算法处理的结果；(c) GE公司VEO算法的重建结果。(d) 和 (e)为图(b)和(c)中感兴趣区域的放大图。

           我们已对上述所提出的CT图像后处理算法申请了多项专利，同时开发了对应的低剂量CT图像后处理软件平台，并成功的应用于南京市第一医院，安徽省医科大学附属第一医院和沈阳军区总医院，截止目前已累计处理病人数据累计达1500例，该算法目前也在安科公司的ANOTOM 16 FIT CT机上获得了初步的应用。

  • 提出基于多尺度和多空间的太赫兹光逆过程求解算法

    •        介于红外线和微波频谱之间的太赫兹光在医学成像中的应用是近10年来的研究热点之一，同X-光等射线相比，太赫兹光具有无辐射的优点，可也有信号穿透能力弱，容易淹没在背景和噪声信号中的弱点，为解决这些问题，申请人于2008年3月至2010年3月间在香港中文大学电子工程系作为博士后研究人员从事太赫兹光的临床医学应用研究，申请人提出了基于多尺度和多空间的处理算法解决太赫兹光的逆过程信号求解和太赫兹光成像问题，并成功的将该方法应用于人体表皮组织的特征函数提取。同时，将稀疏信号表达的理论应用于太赫兹光处理，提高了在太赫兹光信号探测不完整的情况下的对样本特征信号提取以及对应的光成像质量。

    图5. 基于多尺度和多空间的逆过程求解算法在太赫兹信号提取和成像中的应用。

  • 提出基于回溯累加的最小路径的曲线跟踪算法

    •        由于具有求解简单和效率高等特点，最小路径跟踪算法在图形特征检测中有着广泛的应用，如裂痕检测以及血管中轴线提取，然而经典的最小路径算法有着一下两个缺点：a，需要为每个待提取分支提供一个起始点和一个终止点；b，随着搜索过程的进行，算法效率将显著降低， 且容易出现错连的现象。为解决这一问题，被推荐人提出基于回溯累加的最小路径的曲线跟踪算法，圆满的解决了传统的最小路径算法的这二个问题，并在心血管中轴线提取中获得了较好的应用，目前在尝试设计基于最小路径回溯的斑块检测算法，力图实现在此算法框架下的心血管斑块的自动检测。

    图6. 基于回溯累加的最小路径算法的结果,左图为原图(其中红点为设置的种子点)，右图为从左图提取的曲线结构特征。

    AWARDS

  • 2022年国家杰出青年人才
  • 2021年获得江苏省科学技术奖三等奖
  • 2020年获得上海市科技进步奖一等奖并收到联影公司感谢信、中国体视学学会科学技术奖获奖项目科技进步二等奖、获得第十届吴文俊人工智能科技进步奖
  • 2019年获得山东省科技进步奖一等奖
  • 2018年分别获得中国图象图形学会科学技术奖一等奖和辽宁省科技进步奖二等奖
  • 2017年获得江苏医学科技二等奖、获得“2014年东南大学优秀青年教师”、及“2017年东南大学最受欢迎研究生导师”等

    • 研究成果

  • 一种基于高斯极大似然估计的冲激噪声抑制方法 （陈阳，袁文龙，石路遥，罗立民，鲍旭东等，专利号：201410291248.8）
  • 基于改进GPU并行的快速非局部均值滤波算法（陈阳，庄志昆，罗立民，李松毅，鲍旭东等，专利号：201410052166.8）
  • 一种基于三维区别性特征表示的低剂量CT图像分解方法 （陈阳，刘进，罗立民，专利号：2015091700959930.
  • 一种基于回溯累加的曲线检测方法 (陈阳，曹清，罗立民， 李松毅，鲍旭东(201410271291.8)
  • 一种基于三维投影图区别性特征表示的低剂量CT成像方法(陈阳，刘进，罗立民，李松毅，鲍旭东, 2016107496115)
  • 一种CT图像的投影弦图修补方法（陈阳，李印生，罗立民 等，专利号：201010595896.4）
  • 基于小波空间方向性滤波的低剂量CT图像处理方法（陈阳，杨舟，罗立民等，专利号： 2011100377112）
  • 一种基于区别性字典的低剂量CT图像处理算法 （陈阳，石路遥，罗立民等，专利号：201310422085.8）
  • 一种低剂量CT图像滤波方法 （陈阳，罗立民等，专利号：201310122193.3）
  • 一种基于三维区别性特征表示的低剂量CT图像重建方法 （陈阳，刘进，罗立民等，专利号：201510590901.5）
  • 一种基于回溯累加的曲线检测方法 (陈阳，曹清，罗立民， 李松毅，鲍旭东(201410271291.8)
  • 一种基于伽马先验的稀疏角度CT图像重建方法 （陈阳，张俊峰，罗立民等，专利号：201510391071.3）
  • 低剂量CT图像处理软件1.0 简称 ListProbe 1.0（软件著作权，陈阳为第一著作人）

    • 发表论文

    SCI

      2022

      1. Comouted tomographic parameters core ate with coaquation o sorcers in isolate taumare brain injury W Cui, Y Shi, B zhao,J Luo, G Zhu, H Guo, B Wang, C Yang,Z Li, L Wang International Journal of Neuroscence 132(8),835-842

      2. Semi-supervised medical image segmentation via uncertainty rectifed pyramid consistency X Luo. G Wang. W Liao. J Chen. T Song,Y Chen, S Zhang, DN Metavas Medical Image Anaives 80, 10251

      3. Deep learning-based framework for segmentation of multiclass no fractures in CT utilizing a multi-angle projection network Y Ga0. H Chen, R Ge, Z Wu, H Tang, D Gao, X Mai, L Zhang, B Yang,International Journal of Computer Assistant Radiology and Survay.

      4. Low-dose CT imaging via cascaded ResUnet with spectrum loss J Liu, Y Kang, J Qiang,Y Wang, D Hu, Y Chen Methods 202. 78-87.

      5. CCE-Net: A rib fracture diagnosis network based on contralateral, contextual, and edge enhanced modules Y Gao, H Liu, L Jiang, C Yang. X Yin. JL Coatrieux, Y Chen Biomedical Signal Processing and Control 75,103626

      6. Differental diagnosis of primary thyroid lymphoma and papillary thyroid carcinoma by ultrasound combined with computed tomography D Xu, C Peng, D Yi, Y Zhou, J Yao, B Chen, C Yang

      7. Projection network with Spatio-temporal information: 2D+ time DSA to 2D aorta segmentation W Sun, Y He, R Ge, G Yarg, Y Chen. H Shu Multimmedia Tools and Applications, 1-15.

      8. Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet K Chen, LB Zhang, JS Liu, Y Gao, Z Wu, HC Zhu, CP Du, XL Mai,Nuclear Science and Techniques 33(3),1-15.

      9. ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps H Zhu, Y Chen, T Tang, G Ma, J Zhou, J Zhang, S Lu, F wu, L Luo, S Liu, Computer Methods and Programs in Biomedicine 215, 106630

      10. Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge S Ali, N Ghatwary, D Jha, E Isik-Polat, G Polat, C Yang. W Li, A Galdran,arXiv preprint arXiv:2202.12031.

      11. Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method Z Xu, Q Yang, M Li, J Gu. C Du. Y Chen, B Li Frontiers in ancology 12. 829041

      12. DIRECT-Net: A unified mutual-domain matenal decomposition network for quantitative dual -energy CT imaging T Su. X Sun. J Yang. D Mi. Y Zhang. H Wu. S Fang. Y Chen. H Zheng Medical Physics 49 (2). 917-934

      13. X-CTRSNet: 3D cervical vertebra CT reconstruction and segmentation directly from 2D X-ray images R Ge, Y He, C Xia, C Xu, W Sun, G Yang, J Li, Z Wang, H Yu, D Zhang Knowledge Based Systems 236, 107680.

      14. EE-Net: An edge-enhanced deep learning network for jointly identifying corneal micro-layers from optical coherence tomograpy L Wang. M Shen. C Shi. Y Zhou, Y Chen, J Pu, H Chen, Biomedical Signal Processing and Control 71,103213.

      15. Edge prior guided dictionary learning for quantiauve susceptibility mapping reconstruction J Du. Y Ji. J Zhu. X Mai.J Zou. Y Chen. N Gu. Quantitative Imaging in Medicine and Surgery 12 (1), 510.

      2021

      1. Zhan Wu, Rongjun Ge, Minli Wen, Gaoshuang Liu, Chen Yang*, Pinzheng Zhang, Xiaopu He, Jie Hua, Limin Luo and Shuo Li "ELNet: Automatic classification and segmentation for esophageal lesions using convolutional neural network." Medical image analysis 2022

      2. Yu, Xiang, C. Kang, D. Guttery, Seifedine Kadry, Yang Chen and Y. Zhang. “ResNet-SCDA-50 for Breast Abnormality Classification."ResNet-SCDA-50 for Breast Abnormality Classification.IEEE/ACM Transactions on Computational Biology and Bioinformatics

      3. Wenjun Xia, Zexin Lu, Yongqiang Huang, Zuoqiang Shi, Yan Liu, Hu Chen, Yang Chen, Jiliu Zhou, Yi ZhangMAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction", IEEE Transactions on Medical Imaging. (Early Access)

      4. , Haichen Zhu, Yang Chen*, et al,An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging,NeuroImage: Clinical, 2021. (accepted)

      5. Jiashun Liu, Yang Chen*, et al,Rollback Reconstruction for TDC Enhanced Perfusion Imaging,Nuclear Science and Techniques, 2021. (Accepted)

      6. Yikun Zhang, Yang Chen*, et al,CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for Fine-Grains Enhanced Low-Dose CT Imaging, IEEE Transactions on Medical Imaging (In revision)

      7. Dianlin Hu, Yang Chen*, et al,SPECIAL: Single-shot Projection Error Correction Integrated Adversarial Learning for Limited-angle CT Reconstruction, IEEE Transactions on Computational Imaging. (In revision)

      8. Zechen Yu, Yang Chen*, et al,An automated ASPECTS method with atlas-based segmentation, Computer Methods and Programs in Biomedicine. (In revision)

      9. Yuting He, Yang Chen, et al,Few-shot Learning for Deformable Medical Image Registration with Perception-Correspondence Decoupling and Reverse Teaching, IEEE Journal of Biomedical and Health Informatics(In revision)

      10. Rongjun Ge, Yang Chen*, et al,X-CTRSNet: 3D Cervical Vertebra CT Reconstruction and Segmentation Directly from 2D X-Ray Images, Knowledge-Based Systems(In revision)

      11. BangZhong Gu, Rongjun Ge, Yang Chen*,Automatic and robust object detection in X-Ray baggage inspection using deep convolutional neural networks. IEEE Transactions on industrial electronics, 68(10) 1-10 et al.

      2020

      1. Haichen Zhu, Dan Tong, Lu Zhang, Shijie Wang, Weiwen Wu, Hui Tang, Yang Chen*, Limin Luo, Jian Zhu*, Baosheng Li. Temporally Downsampled Cerebral CT Perfusion Image Restoration Using Deep Residual Learning.International Journal of Computer Assisted Radiology and Surgery,2020, 15(2): 193-201. (https://doi.org/10.1007/s11548-019-02082-1)

      2. Dianlin Hu, Weiwen Wu, Moran Xu, Yanbo Zhang, Jin Liu, RongJun Ge, Yang Chen*, Limin Luo, Gouenou Coatrieux.SISTER: Spectral-Image Similarity-based Tensor with Enhanced-sparsity Reconstruction for Sparse-view Multi-energy CT.IEEE Transactions on Computational Imaging, 2020, 6: 477-490. (In Press)

      3. Rongjun Ge, Guanyu Yang, Yang Chen*, Limin Luo, Cheng Feng, Hong Ma, Junyi Ren, Shuo Li*. K-Net: Integrate Left Ventricle Segmentation and Direct Quantification of Paired Echo Sequence.IEEE Transactions on Medical Imaging, 2020, 39(5):1690-1702.

      4. Yunbo Gu, Hui Tang, Tianling Lv, Yang Chen*, Zhiping Wang, Lu Zhang, Jian Yang, Huazhong Shu, Limin Luo, Gouenou Coatrieux. “Discriminative Feature Representation for Noisy Image Quality Assessment.” Multimedia Tools and Applications, 2020, 79(1):7783-7809. (In Press)

      5. Yang Li, Zuyi Yu,Yang Chen, Chunfeng Yang, Yue Li, Ai,“Automatic Seizure Detection using Fully Convolutional Nested LSTM.”INTERNATIONAL JOURNAL OF NEURAL SYSTEMS,2020,30 (4):XXX-XXX.(In Press)

      6. Jianpeng Qiu, Tianling Lyu, Yang Chen*, Shoujun Zhou, Liudong Xing, “An improved matrix-based endovascular guidewire position simulation using fusiform ternary tree.”International Journal of Medical Robotics and Computer Assisted Surgery, 2020,16(4): XXX-XXX. (In Press)

      7. Zhou, Guangquan, Enze Huo, Mei Yuan, Ping Zhou, R. Wang, K. Wang, Yang Chen and X. He. “A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images.” , 2020, 67(12): 2531-2542. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

      8. Yang, Guanyu, Tianling Lv, Yunpeng Shen, S. Li, Jian Yang, Yang Chen, H. Shu, L. Luo and J. Coatrieux. "Vessel Structure Extraction using Constrained Minimal Path Propagation." Artificial intelligence in medicine

      9. Wu, Z., Gonglei Shi, Y. Chen, Fei Shi, Xinjian Chen, G. Coatrieux, J. Yang, L. Luo and Shuo Li. “Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network.” Artificial intelligence in medicine, 2020, 108: 101936.

      10. Yang Guanyu*, Wang Chuanxia, Yang Jian, Chen Yang*, Tang Lijun, Shao Pengfei, Dillenseger Jean-Louis, Shu Huazhong, Luo Limin, Weakly-supervised convolutional neural networks of renal tumor segmentation in abdominal CTA images, Bmc Medical Imaging, 2020, 20(1): 1471-2342.

      11. Zhang Jiulou, Gu Yunbo, Tang Hui, Wang Xiaoqing, Kong Youyong, Chen Yang*, Shu Huazhong*, Coatrieux Jean-Louis, Compressed sensing MR image reconstruction via a deep frequency-division network, Neurocomputing, 2020, 384(346): 1872-8286.

      12. ZLiu Gaoshuang#, Hua Jie#, Wu Zhan, Meng Tianfang, Sun Mengxue*, He Xiaopu, Sun Weihao, Li Xueliang, Chen Yang, Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network, Annals of Translational Medicine, 2020, 8(7): 2305-5839.

      13. Wu Junfeng*, Wang Xiaofeng, Mou Xuanqin, Chen Yang, Liu Shuguang, Low Dose CT Image Reconstruction Based on Structure Tensor Total Variation Using Accelerated Fast Iterative Shrinkage Thresholding Algorithm, Sensors, 2020, 20(6): 1424-8220.

      2019

      1. Yufeng Gao, Yu Song, Xiangrui Yin, Weiwen Wu, Lu Zhang, Yang Chen*, Wanyin Shi*. “Deep Learning-based Digital Subtraction Angiography Image Generation.”International Journal of Computer Assisted Radiology and Surgery, 2019, 14(10):1775-1784. (https://doi.org/10.1007/s11548-019-02040-x)

      2. Tianling Lv, Guanyu Yang, Yudong Zhang, Jian Yang, Yang Chen*, Huazhong Shu, Limin Luo. “Vessel Segmentation Using Centerline Constrained Level Set Method.” Multimedia Tools and Applications, 2019, 78(12): 17051-17075. (https://doi.org/10.1007/s11042-018-7087-x)

      3. Rongjun Ge, Guanyu Yang, Yang Chen*, Limin Luo, Cheng Feng, Heye Zhang, Shuo Li*. “PV-LVNet: Direct Left Ventricle Multitype Indices Estimation from 2D Echocardiograms of Paired Apical Views with Deep Neural Networks.” Medical Image Analysis, 2019, 58: 101554. (https://doi.org/10.1016/j.media.2019.101554)

      4. Fei Shi, Ning Cai, Yunbo Gu, Dianlin Hu, Yuhui Ma, Yang Chen*, Xinjian Chen*. “DeSpecNet: a CNN-based Method for Speckle Reduction in Retinal Optical Coherence Tomography Images.” Physics in Medicine & Biology, 2019, 64(17): 175010.

      5. Xiangrui Yin, Qianlong Zhao, Jin Liu, Wei Yang, Jian Yang, Guotao Quan, Yang Chen*, Huazhong Shu, Limin Luo, Jean-Louis Coatrieux. “Domain Progressive 3D Residual Convolution Network to Improve Low Dose CT Imaging.” IEEE Transactions on Medical Imaging, 2019. (https://doi.org/10.1109/TMI.2019.2917258)

      6. Jin Liu, Yi Zhang, Qianlong Zhao, Tianling Lv, Weiwen Wu, Ning Cai, Guotao Quan, Wei Yang, Yang Chen*, Limin Luo, Huazhong Shu, Jean-Louis Coatrieux. “Deep Iterative Reconstruction Estimation (DIRE): Approximate Iterative Reconstruction Estimation for Low Dose CT Imaging.” Physics in Medicine & Biology, 2019. (https://doi.org/10.1088/1361-6560/ab18db)

      7. Jianpeng Qiu, Libo Zhang, Guanyu Yang, Yang Chen*, Shoujun Zhou*. “An Improved Real-Time Endovascular Guidewire Position Simulation Using Activity on Edge Network.” IEEE Access, 2019, 7: 126618-126624. (https://doi.org/10.1109/ACCESS.2019.2935327)

      8. 刘进, 赵倩隆, 尹相瑞, 顾云波, 康季槐, 陈阳*. “基于特征学习的低剂量CT成像算法研究进展.” CT理论与应用研究, 2019, 28(3): 393-406. (doi:10.15953/j.1004-4140.2019.28.03.14)

      9. 刘进, 亢艳芹, 顾云波, 陈阳*. “稀疏张量约束的低剂量CT图像重建.” 光学学报, 2019, 39(8): 1-10. (doi:10.3788/AOS201939.0811004)

      2018

      1. Wei Yang#, Liming Zhong#, Yang Chen, Liyan Lin, Zhentai Lu, Shupeng Liu, Yao Wu, Qianjin Feng*, Wufan Chen. Predicting CT Image from MRI Data through Feature Matching with Learned Nonlinear Local Descriptors. IEEE Transactions on Medical Imaging, 2018, 37(4): 977-987. (SCI, IF: 6.13)

      2. Yang W , Zhong L , Chen Y , et al. Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors[J]. IEEE Transactions on Medical Imaging, 2018, 37(4):977.

      3. Longyu Jiang, Runguo He, Jie Liu, Yang Chen, Jiasong Wu, Huazhong Shu, Jean-Louis Coatrieux. Phase-Constrained Parallel Magnetic Resonance Imaging Reconstruction Based on Low-Rank Matrix Completion, IEEE Access, vol. 6, pp. 4941-4954, 2018.

      4. M Outtas, L Zhang, O Deforges, A Serir, W Hamidouche and Y Chen*.Subjective and objective evaluations of feature selected multi output filter for speckle reduction on ultrasound images.Physics in medicine and biology , 2018

      5. Jin Liu, Yining Hu, Jian Yang, Yang Chen*, Huazhong Shu, Limin Luo, Qianjing Feng, Zhiguo Gui, Gouenou Coatrieux “3D Feature Constrained Reconstruction for Low Dose CT Imaging,” IEEE Transactions on Circuits and Systems for Video Technology (10.1109/TCSVT.2016.2643009).

      6. Wei Yang, Huijuan Zhang, Yang Chen*, et al.Improving Low-dose CT Image Using Residual Convolutional NetworkIEEE Access, vol. 5, pp. 24698-24705, 2017.

      2017

      1. Wei Yang, Huijuan Zhang, Yang Chen* , et al. Improving Low-dose CT Image Using Residual Convolutional Network 10.1109/ACCESS.2017.2720418

      2. Yang Chen , Jin Liu, Huazhong Shu*, et al. Discriminative Prior - Prior Image Constrained Compressed Sensing Reconstruction for Low-Dose CT Imaging Scientific Reports 7, Article number: 13868(2017)

      3. Yang Chen , Lu Zhang, et al. Extended PCJO for the detection-localization of hypersignals and hyposignals in CT images 10.1109/ACCESS.2017.2766438

      4. Yang Chen, Jin Liu, Yining Hu, Jian Yang,Luyao Shi, Huazhong Shu, Zhiguo Gui,Gouenou Coatrieux and Limin Luo.“Discriminative feature representation: an effective postprocessing solution to low dose CT imaging” Phys. Med. Biol. 62 (2017) 2103

      5. Jin Liu，Yang Chen* Discriminative Feature Representation to Improve Projection Data Inconsistency for Low Dose CT Imaging IEEE, Transaction on Medical Imaging, 10.1109/TMI.2017.2739841.

      6. Hu Chen, Yi Zhang*，Yang Chen et al. Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)， IEEE, Transaction on Medical Imaging, 10.1109/TMI.2017.2715284.

      2016

      1. Jin Liu, Yining Hu, Jian Yang, Yang Chen*, Huazhong Shu, Limin Luo, Qianjing Feng, Zhiguo Gui, Gouenou Coatrieux “3D Feature Constrained Reconstruction for Low Dose CT Imaging,” IEEE Transactions on Circuits and Systems for Video Technology (10.1109/TCSVT.2016.2643009).

      2. Yang Chen, Budde Adam, Ke Li, Yinsheng Li, Jiang Hsieh, and Guang-Hong Chen, “A platform-independent method to reduce CT truncation artifacts using discriminative dictionary representations,” Medical Physics.(doi:10.1002/mp.12032)

      3. Junfeng Zhang; yining hu; jian yang; Yang Chen*; Jean-Louis Coatrieux; Limin Luo et al. “Sparse-view X-ray CT Reconstruction with Gamma Regularization,” Neurocomputing (http://dx.doi.org/10.1016/j.neucom.2016.12.019).

      4. Bin Chen，Yang Chen*，Limin Luo et al. “ Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing,” Neurocomputing 200(2016)110–117.

      5. Yang Chen, Jian Yang ; Yudong Zhang ; Huazhong Shu ; Limin Luo ; Coatrieux Jean-Louis ; Qianjing Feng. “Structure-adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression,” IEEE Transactions on Circuits and Systems for Video Technology (10.1109/TCSVT.2016.2615444).

      6. Luyao Shi, Yang Chen*, Huazhong Shu, Limin Luo, Jean-Louis Coatrieux, et al. “Improving Low-dose Cardiac CT Images based on 3D Sparse Representation Scientific Reports 6:22804 | DOI: 10.1038/srep22804 .

      7. Guanyu Yang, Yang Chen, Huazhong Shu, et al. “Automatic Coronary Calcium Scoring using Non-Contrast and Contrast CT Images” Medical Physics, 2016, 43(5):2174-2186.

      8. Yang Chen, Yudong Zhang, Jian Yang, Qing Cao, Guanyu Yang, Jian Chen, Huazhong Shu, Limin Luo, Jean-Louis Coatrieux, Qianjing Feng. “Curve-like Structure Extraction Using Minimal Path Propagation with Backtracking,” IEEE, Transaction on Image Processing, 25(2), pp. 988-1003, 2016

      2015

      1. Yuanjin Li, Huazhong Shu, Yang Chen*, Tao Wang, Zuogang Yue, Yang Wang, “Research on XRII image distortion correction based on biharmonic spline surface interpolation,” Journal of Fiber Bioengineering and Informatics, 8(2), pp. 329-336, 2015

      2. Luyao Shi, Xindao Yin, Libo Zhang Benqiang Yang, Jie Zhan, Yang Chen*, HuaZhong Shu, Limin Luo, “Improving low-dose brain perfusion computed tomography using 3D dictionary learning based processing,” J. Med. Imaging Health Inf., 5(7), pp. 1-5, 2015.

      3. Libo Zhang, Benqiang Yang, Zhikun Zhuang, Yining Hu, Yang Chen*, Limin Luo, Huazhong Shu. “Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing,” Computational and Mathematical Methods in Medicine, vol. 2015, Article ID 790313, 11 pages, 2015.

      4. Junfeng Zhang, Yang Chen*, Yining Hu, Limin Luo, Huazhong Shu, Bicao Li, Jin Liu, Coatrieux, Jean-Louis. “Gamma regularization based reconstruction for low dose CT,” Physics in medicine and biology, 60(17): 6901-6921, 2015.

      5. Weijian Cong, Jian Yang, Danni Ai, Yang Chen, Yue Liu, Yongtian Wang, “Quantitative Analysis of Deformable Model based 3-D Reconstruction of Coronary Artery from Multiple Angiograms,” IEEE Transactions on Biomedical Engineering, 62(8), pp. 2079-2090，2015.

      2014

      1. Si Li, Qing Cao, Yang Chen*, Yining Hu, Limin Luo, Toumoulin, Christine. “Dictionary learning based sinogram inpainting for CT sparse reconstruction,” Optik, 125(12), pp. 2862-2867, 2014

      2. Yang Chen, Luyao Shi, Jian Yang, Yining Hu, Limin Luo, Xindao Yin, Coatrieux, Jean-Louis. “Radiation dose reduction with dictionary learning based processing for head CT,” Australasian Physical & Engineering Sciences in Medicine, 37(3), pp. 483-93, 2014.

      3. Yang Chen，Luyao Shi，Qianjing Feng，Jian Yang，Huazhong Shu，Limin Luo，Coatrieux, J.-L.， Wufan Chen. “Artifact Suppressed Dictionary Learning for Low-dose CT Image Processing,” IEEE, Transaction on Medical Imaging, 33(12), pp.2271-2292, 2014.

      4. Xuehu Wang, Jian Yang, Yang Chen, Danni Ai, Yining Hu, Yongtian Wang, “Optimal Viewing Angle Determination for Multiple Vessel Segments in Coronary Angiographic Image,” IEEE Transactions on Nuclear Science, 61(3), pp. 1290-1303,2014

      5. Danni Ai, Jian Yang, Yang Chen, Weijian Cong, Jingfan Fan, Yongtian Wang, “Multiresolution Generalized N Dimension PCA for Ultrasound Image Denoising,” Biomedical Engineering Online, 13:112, 13.1 (2014): 112.

      6. Yang Chen, Jian Yang, Huazhong Shu, Luyao Shi, Jiasong Wu, Limin Luo, Coatrieux, Jean-Louis., Toumoulin, Christine. “2-D Impulse Noise Suppression by Recursive Gaussian Maximum Likelihood Estimation,” PLoS ONE, 9(5): e96386，2014.

      7. Jian Yang, Weijian Cong, Yang Chen, Jingfan Fan, Yue Liu and Yongtian Wang, “External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction,” Physics in Medicine and Biology, 59(4), pp. 975–1003, 2014.

      8. Jian Yang, Yang Chen, Jingfan Fan, Danni Ai, Songyuan Tang, “Nonrigid Medical Image Registration by Maxwell Model of Viscoelasticity,” Abstract and Applied Analysis 2014.

      2013

      1. Yang Chen, Xindao Yin, Luyao Shi, Huazhong Shu, Limin Luo, Coatrieux, Jean-Louis, Toumoulin, Christine, “Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing,” Physics in Medicine and Biology, 58(16), pp. 5803–5820, 2013. (3/8)

      2. Jun Wang, Shijie Wang, Yang Chen, Jiasong Wu, Coatrieux, Jean-Louis, Limin Luo. “Metal artifact reduction in CT using fusion based prior image,” Medical Physics, 40(8):081903, 2013.

      3. Quan Zhang, Zhiguo Gui, Yang Chen, Yuanjin Li, Limin Luo, “Bayesian sinogram smoothing with an anisotropic diffusion weighted prior for low-dose X-ray computed tomography,” Optik-International Journal for Light and Electron Optics, 124(17), pp. 2811-2816, 2013.

      2012

      1. Yang Chen, Yinsheng Li , Hong Guo, Yining Hu, Limin Luo, Xindao Yin, Jianping Gu, Toumoulin, Christine. “CT Metal Artifact Reduction Method Based on Improved Image Segmentation And Sinogram In-painting,” Mathematical Problems in Engineering, vol. 2012, Article ID 786281, 2012.

      2. Yang Chen, Zhou Yang, Yining Hu, Guangyu Yang, Yongcheng Zhu, Yinsheng Li, Limin Luo, Wufan Chen, Toumoulin, Christine. “Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means,” Physics in Medicine and Biology, 57(9), pp. 2667–2688, 2012.

      3. Yinsheng Li, Yang Chen*, Yining Hu, Oukili, Ahmed, Limin Luo, Wufan Chen, Toumoulin, C. “A strategy of CT sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation,” J. Opt. Soc. Am. A, 29 (1), pp. 153-163, 2012.

      2011

      1. Hao Wu, Wenhua Zhang, Dazhi Gao, Xindao Yin, Yang Chen, Weidong Wang. “Fast CT image processing using parallelized non-local means,” Journal of Medical and Biological Engineering, 31(6), pp. 437-441, 2011

      2. Yang Chen, Yinsheng Li, Weimin Yu, Limin Luo, Wufan Chen, Toumoulin, Christine. “Joint-MAP Tomographic Reconstruction with Patch Similarity Based Mixture Prior Model,” SIAM, Multiscale Modeling and Simulation, 9 (4), pp. 1399-1419, 2011.

      3. Yang Chen, Wufan Chen, Xindao Yin, Xianghua Ye, Xudong Bao, Limin Luo, Qianjing Feng, Yinsheng Li, Xiaoe Yu. “Improving Low-dose Abdominal CT Images by Weighted Intensity Averaging over Large-scale Neighborhoods,” European Journal of Radiology, 80(2), pp. e42-e49, 2011.

      4. Yuanjin Li, Yang Chen, Limin Luo, “Fast CT metal artefacts correction based on derivative and region-based flling,”Journal of Medical Imaging and Radiation Oncology, 55(6), pp. 535–541, 2011.

      5. Yang Chen, Weimin Yu, Yinsheng Li, Zhou Yang, Limin Luo, and Wufan Chen, “Bayesian Image Restoration Using a Large-Scale Total Patch Variation Prior,” Mathematical Problems in Engineering, vol. 2011, Article ID 408241, 2011.

      6. Yang Chen, Yiwen Sun, E. Pickwell-Macpherson. “Total variation deconvolution for terahertz pulsed imaging,” Inverse Problems in Science & Engineering, 19(2), pp. 223-232, 2011.

      2010

      1. Yang Chen, Shengyang Huang, and Emma Pickwell-MacPherson, “Frequency-wavelet domain deconvolution for terahertz reflection imaging and spectroscopy,” Optical Express, 18(2), pp. 1177-1190, 2010.

      2. Yang Chen, Yinsheng Li, Yingmei Dong, Liwei Hao, Limin Luo, and Wufan Chen, “Effective Image Restorations Using a Novel Spatial Adaptive Prior, “ EURASIP Journal on Advances in Signal Processing Volume 2010, Article ID 508089, 2010.

      3. Yang Chen，E. Pickwell-Macpherson , “Improving extraction of impulse response functions using Stationary Wavelet Shrinkage in Terahertz reflection imaging,” Fluctuation and Noise Letters, 9(4), pp. 387-394, 2010.

      2009

      1. S.Y. Huang, P. C. Ashworth, K. W. C. Kan, Yang Chen, V. P. Wallace, Y. T. Zhang, and E. Pickwell-MacPherson. “Improved sample characterization in terahertz reflection imaging and spectroscopy,” Optical Express, 17(5), pp. 3848-3854, 2009.

      2. Yang Chen, Dazhi Gao, Cong Nie, Limin Luo, Wufan Chen, Xindao Yin, Yazhong Lin,. “Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior,” Computerized Medical Imaging and Graphics, 33(7), pp. 495-500, 2009.

      3. Yang Chen, Liwei Hao, Xianghua Ye, Wufan Chen, Limin Luo, Xindao Yin, “PET Transmission Tomography Using A Novel Nonlocal MRF Prior,” Computerized Medical Imaging and Graph, 33(8), pp. 623-633, 2009.

      2008

      1. Yang Chen, Jianhua Ma, Qianjin Feng, Limin Luo, Pengcheng Shi, Wufan Chen. “Nonlocal Prior Bayesian Tomographic Reconstruction,” Journal of Mathematical Imaging and Vision, 30(2), pp.133-146, 2008.

      2006

      1. Yang Chen, Wufan Chen, Yanqiu Feng and Qianjin Feng”Convergent Bayesian Reconstruction for PET Using New MRF Quadratic Membrane-Plate Hybrid Multi-order Prior,”LECTURE NOTES IN COMPUTER SCIENCE，Volume4091/2006: 309-316. Medical Imaging and Augumented Reality.

      EI

      2017

      1. Jiasong Wu, Shijie Qiu, Youyong Kong, Yang Chen, Lotifi Senhadji, Hhuazhong Shu, MomentsNet: A Simple Learning-free Method for Binary Image Recognition, 2017 IEEE International Conference on Image Processing, IEEE ICIP 2017, 2667-2671, 2017.9.17-2017.9.20.

      2016

      1. J. Zhang, Y. Chen and L. Luo, Improved Nonlocal Means for Low-Dose X Ray CT Image, 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), Beijing, 2016, pp. 410-413.

      2. J. Liu, Y. Chen, Y. Hu and L. Luo, Low-dose CBCT reconstruction via 3D dictionary learning, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, 2016, pp. 735-738.

      3. B. Chen, Y. Chen, Z. Shao and L. Luo, Retinal vessel enhancement using multi-dictionary and sparse coding, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 893-897.

      4. Chen Y, Li K, Li Y, et al. Reduction of truncation artifacts in CT images via a discriminative dictionary representation method[C]//SPIE Medical Imaging. International Society for Optics and Photonics, 2016: 97831D-97831D-7.

      2015

      1. Bin Chen, Yang Chen, Guanyu Yang, Jingyu Meng, Rui Zeng, Limin Luo, “Segmentation of liver tumor via nonlocal active contours,” IEEE. ICIP 2015, pp.3745-3748, 2015

      2. Luyao Shi, Yang Chen, Limin Luo. “Improving low-dose cardiac CT images using 3D sparse representation based processing,” Proc. SPIE 9412, Medical Imaging 2015

      2014

      1. Yuanjin Li, Huazhong Shu, Limin Luo, Yang Chen, Tao Wang, Zuogang Yue. “Application of the Delaunay triangulation interpolation in distortion XRII image,” Journal of Southeast University (English Edition), 30(3), pp. 306-310, September 1, 2014

      2. Y. Chen, Q. Cao, G. Yang, H. Shu, L. Luo, Toumoulin, Christine, Coatrieux, Jean-Louis, “Centerline constrained minimal path propagation for vessel extraction”, IEEE ISBI 2014, pp. 794-797, 2014.

      3. Luyao Shi, Yang Chen, Huazhong Shu, Limin Luo, Toumoulin, C., Coatrieux, J.-L, “Low-dose CT Image Processing Using Artifact Suppressed Dictionary Learning”IEEE ISBI 2014, pp. 127-130, 2014.

      4. Zhikun Zhuang, Yang Chen, Huazhong Shu, Limin Luo, Christine Toumoulin, Jean-Louis Coatrieux, “Fast Low-dose CT Image Processing Using Improved parallelized Nonlocal Means Filtering,” IEEE ICMB 2014, pp.147-150, 2014.

      5. Xu Zhang, Jia Song Wu, Yang Chen, Guan Yu Yang, Coatrieux, J.L., Hua Zhong Shu. “Coronary cine-angiography segmentation incorporating low-rank priori,”Biomedical Engineering and Informatics (BMEI), 2014 7th International Conference on Year: 2014. pp. 168 – 173.

      6. Jiasong Wu, Longyu Jiang, Yang Chen, Senhadji, L., Huazhong Shu, “A generalized modified split-radix FFT algorithm for N=q×2m and its applications,” Image and Signal Processing (CISP), 2014 7th International Congress on Year: 2014. pp. 799 – 803.

      7. Guanyu Yang, Yang Chen, Lijun Tang, Huazhong Shu, Toumoulin, C., “Automatic left ventricle segmentation based on multiatlas registration in 4D CT images,” Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on Year: 2014. pp. 413 – 416.

      2013

      1. Q. Cao, Y. Chen, G. Yang, H. Shu, L. Luo, “Coronary vessel extraction method using an improved minimum path based region growing,” IEEE BMEI, pp. 127-150, 2013.

      2. Y. Chen, Y. Fei, L. Luo and C. Toumoulin, “Improving abdomen tumor low-dose CT images using dictionary learning based patch processing and unsharp filtering,” IEEE EMBS 2013, pp. 4014-4017,2013.

      3. Yang Chen, Luyao Shi, Yining Hu, Qing Cao, Fei Yu, Limin Luo, Toumoulin, C, “Confidence WeightedDictionary LearningAlgorithmfor Low-dose CT Image Processing,” IEEE NSS-MIC 2013.

      4. Fei Yu, Yang Chen, Limin Luo, “CT image denoising based on sparse representation using global dictionary”, Complex Medical Engineering (CME), 2013 ICME International Conference on Year: 2013. pp. 408 - 411

      5. Yining Hu, Lizhe Xie, Yang Chen, Qing Cao, Limin Luo, Toumoulin,C., “Adaptive. L0 norm constrained reconstructions for sparse-view scan in cone-beam CT,” Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE Year: 2013. pp. 1 – 4.

      6. Yang Chen, Qing Cao, Zhikun Zhuang, Zhou Yang, Limin Luo, Toumoulin, Christine. “3-D coronary vessel extraction using a novel minimum path based region growing,” Lecture Notes in Computer Science, v7950 LNCS, p502-509, 2013, Image Analysis and Recognition - 10th International Conference, ICIAR 2013, Proceedings

      7. Yang Chen, Luyao Shi, Yining Hu, Qing Cao, Fei Yu, Limin Luo, Toumoulin, C, “Confidence Weighted Dictionary Learning algorithm for low-dose CT image processing,” Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2013 IEEE Year: 2013. pp. 1 – 4.

      2012

      1. 朱永成，陈阳*，罗立民，“基于字典学习的低剂量X-ray CT图像去噪，” 东南大学学报（自然科学版），42(5), 864-868, 2012

      2011

      1. Weiming Yu, Yang Chen, Limin Luo, “Denoising of low-dose CT images using space-time nonlocal means over large-scale neighborhoods,” IEEE/ICME International Conference on Complex Medical Engineering, pp. 455-459, 2011.

      2010

      1. Yang Chen, XuDong Bao, Xindao Yin, Limin Luo, Wufan Chen, “ Improving low-dose X-ray CT images by Weighted Intensity Averaging over Large-scale Neighborhoods,” IEEE/CISP Congress on Image and Signal Processing, pp. 727-729, 2010.

      2. Yinsheng Li, Yang Chen, Xudong Bao, Limin Luo, “Metal artifact reduction in CT based on adaptive steering filter and nonlocal sinogram inpainting,” IEEE/BMEI Congress on Biomedical Engineering and Informatics, pp. 380-383, 2010.

      3. Lizhe Xie, Yining Hu, Yang Chen, Limin Luo, “Maximun a posteriori based coronary angiograms segmentation method with vessel-like feature and Markov Random Field,” 2010 International Conference on Medical Image Analysis and Clinical Application, MIACA 2010, p 123-126, 2010

      4. 李印生, 陈阳, 罗立民,等. 基于非局部方向性核先验的PET图像Bayesian重建. 东南大学学报：自然科学版, 40(5), 937-942, 2010

      2009

      1. Yang Chen, E. Pickwell-Macpherson, “Stationary-wavelet Regularized Inverse Filtering: A Robust Deconvolution Approach for Terahertz Reflection imaging,” 34rd International Conference on Infrared, Millimeter and Terahertz Waves [C], Busan, 2009.

      2. 陈阳, 吴昊，严勇，陈武凡，罗立民，基于一种新的非局部二次MRF 先验模型的Bayesian图像重建，电子学报．37(4), 744-749, 2009

      2007

      1. Yang Chen, Wufan Chen, Pengcheng Shi and Qianjin Feng, “Joint Bayesian PET Reconstruction Algorithm Using A Quadratic Hybrid Prior,” LECTURE NOTES IN COMPUTER SCIENCE, MIRAGE[C], LNCS 4418, pp. 23 – 35, 2007.

      2. Yang Chen, Wufan Chen, Pengcheng Shi, Qianjin Feng, Yanqiu Feng, Qingqi Wang, Zhiyong Huang, “A Novel Way of Incorporating Large-scale Knowledge into MRF Prior Model,” LECTURE NOTES IN COMPUTER SCIENCE，Artificial Intelligence in Medicine Europe conference, pp. 388-392, 2007.

      3. Yang Chen, Qianjin Feng, Pengcheng Shi, Wufan Chen. “A Novel Nonlocal Quadratic MRF Prior Model for Positron Emission Tomography,” IEEE, ISBI 2007, pp.149-152, 2007.

      4. Yang Chen, Qianjin Feng, et al. “A Novel Method of Correcting The Sinogram for Postrion Emission Tomography,” IEEE ICME 2007, pp. 1375-1378, 2007.

      5. Yang Chen, Qianjin Feng, Wufan Chen, Jie Zhan, Pengcheng Shi, “Bayesian Reconstruction Using A Novel Nonlocal MRF Prior for PET Transmission Tomography,” IEEE ICME 2007, pp. 525-528, 2007.

      6. 陈阳, 陈武凡,冯衍秋,马建华, 基于MRF二次Membrane-Plate混合自适应先验算子的PET图像的收敛的贝叶斯重建算法, 电路与系统学报[J], 12(3), 45-51, 2007。

      7. 陈阳, 陈武凡*, 一种新的基于纠正正弦图探测值的PET图像Bayesian重建算法, 南方医科大学学报, 27(3), 325-328, 2007。

      8. 陈阳, 陈武凡, 冯衍秋, 冯前进, 马建华, 采用MRF二次混合多阶先验算子的PET图像的贝叶斯重建, 中国生物医学工程学报[J], 26(1), 83-88, 2007。

      9. 冯衍秋, 黄鑫, 陈阳, 陈武凡, 基于非局部均值平滑约束的并行磁共振成像重建算法, 南方医科大学学报, 27(8), 1170-1172, 2007。

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