二维码（扫一下试试看！） | | 基于统计形变模型的多模医学图像非刚性配准方法研究 | | Statistical Deformation Model Based Non-Rigid Multimodal Medical Image Registration | | 投稿时间：2018-10-20 | | DOI：10.15918/j.tbit1001-0645.2019.s1.010 | | 中文关键词:多模医学图像非刚性配准统计形变模型目标配准误差 | | English Keywords:multimodal medical imagenon-rigid registrationstatistical deformation modeltarget registration error | | 基金项目:科技部十三五国家重点研发计划资助项目（2017YFB1303100） | | | 摘要点击次数:3968 | | 全文下载次数:296 | | 中文摘要: | | 多模医学图像间可能存在复杂的非刚性形变，矫正这类形变需要采用具有较高自由度的非线性变换模型.直接求解非线性变换的高维参数，不仅会增加配准时间，而且也影响配准精度.为此，本文提出一种基于统计形变模型的配准算法，该算法利用统计形变模型对大量多模图像间的非刚性形变进行统计学习，利用由此建立的模型大幅减少变换模型的参数，达到提高图像配准效率和精度的目的.大量的实验结果表明：与基于传统自由形变模型的配准算法相比，本文提出的基于统计形变模型的配准算法其效率可以提高52%，同时目标配准误差平均减少0.503 2个像素. | | English Summary: | | There may exist the complex non-rigid deformation among multimodal medical images. To correct such deformations, the nonlinear transformation models with a high degree of freedom must be used. Solving the high-dimensional parameters of the nonlinear transformation directly will not only increase registration time but also affect registration accuracy. To solve this problem, a registration method was proposed based on statistical deformation model in this paper. Firstly, a statistical deformation model was established to statistically learn the non-rigid deformation among a large number of multimodal images, and to greatly reduce the number of parameters in the transformation model, to improve image registration efficiency and accuracy. Experimental results show that, compared with the registration method based on traditional free-form deformation model, the efficiency of the proposed statistical deformation model based registration method can be improved by 52%, and the target registration error can be reduced by 0.503 2 pixels. | | 查看全文查看/发表评论下载PDF阅读器 | |
丁明跃,冯瑞敏,黄晶,陈钊正,李晓庆.高频复合超声扫描探针显微镜声学图像融合研究[J].北京理工大学学报（自然科学版）,2019,39(S1):57~61.DINGMing-yue,FENGRui-min,HUANGJing,CHENZhao-zheng,LIXiao-qing.Researchon ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21栾宽,王琭璐,刘小龙,袁浩,李金.基于三维图像的髓内针钉孔定位方法[J].北京理工大学学报（自然科学版）,2019,39(S1):71~76.LUANKuan,WANGLu-lu,LIUXiao-long,YUANHao,LIJin.LocalizationofHolesonIntramedulla ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21程坤,时永刚,李依桐,刘志文.基于生成对抗网络的海马子区图像分割[J].北京理工大学学报（自然科学版）,2019,39(S1):159~163.CHENGKun,SHIYong-gang,LIYi-tong,LIUZhi-wen.ImageSegmentationofHippocampalSubfi ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21宁志刚,郝光鹏,程雄,沈文斌,丁德馨.基于图像分析的堆浸铀矿石颗粒参数辨识[J].北京理工大学学报（自然科学版）,2018,38(3):300~304,312.NINGZhi-gang,HAOGuang-peng,CHENGXiong,SHENWen-bin,DINGDe-xin.Parameter ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21孙华飞,张通,韩希武,李帝东.统计流形上的主丛结构[J].北京理工大学学报（自然科学版）,2018,38(4):437~440.SUNHua-fei,ZHANGTong,HANXi-wu,LIDi-dong.PrincipalBundleStructureofStatisticalManifolds ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21邬春明,郑宏阔,王艳娇,付饶,于明,孙勇.基于多节点协作的WMSNs图像压缩算法[J].北京理工大学学报（自然科学版）,2018,38(5):545~550.WUChun-ming,ZHENGHong-kuo,WANGYan-jiao,FURao,YUMing,SUNYong.AImageCompr ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21高绍姝,张晓东,金伟其.可见光与红外灰度融合图像感知对比度客观评价[J].北京理工大学学报（自然科学版）,2018,38(7):715~720.GAOShao-shu,ZHANGXiao-dong,JINWei-qi.PerceptualContrastMetricforVisibleandInfr ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21刘帅奇,扈琪,李喆,安彦玲,李鹏飞,赵杰.基于相似性验证与子块排序的NSST域SAR图像去噪[J].北京理工大学学报（自然科学版）,2018,38(7):744~751.LIUShuai-qi,HUQi,LIZhe,ANYan-ling,LIPeng-fei,ZHAOJie.SARImageDeno ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21胡海云,王斌容,由艳华,杨晓丽.质子辐照过程的统计分布研究[J].北京理工大学学报（自然科学版）,2018,38(10):1096~1100.HUHai-yun,WANGBin-rong,YOUYan-hua,YANGXiao-li.StudyonStatisticalDistributionofP ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21王晓华,许雪,王卫江,高东红.一种稀疏度拟合的图像自适应压缩感知算法[J].北京理工大学学报（自然科学版）,2017,37(1):88~92.WANGXiao-hua,XUXue,WANGWei-jiang,GAODong-hong.ANovelAlgorithmonAdaptiveImageCom ... 北京理工大学科研学术 本站小编 Free考研考试 2021-12-21
| |
