心脏体素化三维模型感兴趣血管交互式显示方法研究
|
摘要
通过应用线性八叉树将心脏三维模型离散成体素以快速提取并显示局部任意感兴趣血管,把三维模型离散成体素后,利用多边形交互区域选择感兴趣体积内部体素。根据26-邻接体素的拓扑关系和体素互信息相似度比较结果,最终确定最佳深度和最佳互信息阈值分别为14和-1.375,以此来寻找感兴趣体积中同一组织的体素集合。最终实现了三维医学影像中任意感兴趣血管的精确显示,该技术可被视为用一个提取工具将任意局部三维模型进行提取并进行四维动态显示的过程。实验结果表明:与C-V三维分割算法精度90.1%相比,本分割算法平均精确度达到96.02%;运行时间从13.8s缩短为10.7s;四维播放帧数最大40FPS,基本满足了血管三维分割的临床需求。该算法不仅可以快速地分析局部病灶的生理学特点和病理特征,而且让医生更加直观、便利地观察病人心脏任意局部血管的实际运动状况,以便做出临床决策。
In the paper,the linear octree was used to separate the 3 D model of the heart into voxels and to achieve the purpose of rapid display of any interested vessels.After the 3 D model is separated into voxels,the polygon area is used to select inside voxels of VOI.Based on the topological relation of 26-adjacent voxels and the similarity of voxel mutual information,the final optimal depth and best mutual information threshold are confirmed as respectively14 and-1.375,thus discovering the voxel set of the same tissue in the volume of interest.The technology ultimately achieves the precise displayof any VOI from a medical image.This technique can be viewed as a process of extracting any local three-dimensional model with an extraction tool and performing a four-dimensional dynamic display.Experimental results show that compared with C-V 3 Dsegmentation algorithm accuracy of 90.1%,the average accuracy of this segmentation algorithm has reached 96.02%;running time is shortened from 13.8 sto 10.7 s;4 Dplayback framer has reached its maximum 40 FPS,which basically meets the clinical needs of vascular 3 Dsegmentation.It is not only agood way to quickly analyze the physiological and pathological characteristic for the local lesion,but it is more intuitionistic and convenient for doctors to observe the actual condition of any local blood vessel of the patient's heart,which contributes to clinical decision-making.
In the paper,the linear octree was used to separate the 3 D model of the heart into voxels and to achieve the purpose of rapid display of any interested vessels.After the 3 D model is separated into voxels,the polygon area is used to select inside voxels of VOI.Based on the topological relation of 26-adjacent voxels and the similarity of voxel mutual information,the final optimal depth and best mutual information threshold are confirmed as respectively14 and-1.375,thus discovering the voxel set of the same tissue in the volume of interest.The technology ultimately achieves the precise displayof any VOI from a medical image.This technique can be viewed as a process of extracting any local three-dimensional model with an extraction tool and performing a four-dimensional dynamic display.Experimental results show that compared with C-V 3 Dsegmentation algorithm accuracy of 90.1%,the average accuracy of this segmentation algorithm has reached 96.02%;running time is shortened from 13.8 sto 10.7 s;4 Dplayback framer has reached its maximum 40 FPS,which basically meets the clinical needs of vascular 3 Dsegmentation.It is not only agood way to quickly analyze the physiological and pathological characteristic for the local lesion,but it is more intuitionistic and convenient for doctors to observe the actual condition of any local blood vessel of the patient's heart,which contributes to clinical decision-making.
引文
[1]CAO Y H,JIN Q H,CHEN Y D,et al.Automatic identification of side branch and main vascular measurements in intracascular optical coherence tomography images[C].Biomedical Imaging(ISBI 2017),2017IEEE 14th International Symposium on.IEEE,2017:608-611.
[2]韩承航,程云章.基于模糊聚类和改进C-V模型的冠状动脉图像分割方法[J].北京生物医学工程,2017,36(3):262-267.HAN C H,CHENG Y Z.Coronary image segmentation based on fuzzy clustering method and improved C-V model[J].Beijing Biomedical Engineering,2017,36(3):262-267.(in Chinese)
[3]丁蓬莉.基于深度学习的糖尿病性视网膜图像分析算法研究[D].北京:北京交通大学,2017.DING P L.Research of diabetic retinal image analysis algorithms based on deep learning[D].Beijing:beijing jiaotong university,2017.(in Chinese)
[4]徐光柱,张柳,邹耀斌,等.自适应脉冲耦合神经网络与匹配滤波器相结合的视网膜血管分割[J].光学精密工程,2017,25(03):756-764.XU G Z,ZHANG L,ZOU Y B,et al.Retinal blood segmentation with adaptive PCNN and matched filter[J].Optics and Precision Engineering,2017,25(03):756-764.
[5]仇清涛,段敬豪,巩贯忠,等.基于三维动态区域生长算法的肝脏自动分割[J].中国医学物理学杂志,2017,34(7):660-665.QIU Q T,DUAN J H,GONG G Z,et al.Liver auto-segmentation based on three-dimensional dynamic region growing algorithm[J].Chinese Journal of Medical Physics,2017,34(7):660-665.(in Chinese)
[6]钱峰,马秀丽,杨胜齐,等.移动立方体算法的研究和改进[J].计算机工程与应用,2010,46(34):177-180.QIAN F,MA X L,YANG S Q,et al.Research and improvement of marching cubes algorithm[J].Computer Engineering and Applications,2010,46(34):177-180.(in Chinese)
[7]JF O'Brien,NF Ezquerra.Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal,spatial,and structural constraints[J].Visualization in Biomedical Computing,1994,14(2):657-662.
[8]SCHMITT H,GRASS M,RASCHE V,et al.An X-ray-based method for the determination of the contrast agent propagation in 3-D vessel structures[J].IEEE Transactions on Medical Imaging,1996,15(3):377-379.
[9]WANG S,FU Y,YUE Y,et al.Fast and Automatic Segmentation of Ascending Aorta in MSCT Volume Data[J].International Congress on Image&Signal Processing,2009,45(10):1-5.
[10]HUANG P,WANG C C L.Volume and complexity bounded simplification of solid model represented by binary space partition[J].Acm Symposium on Solid&Physical Modeling,2010,12(4):1158-1162.
[11]刘迎,王朝阳,高楠,张宗华.特征提取的点云自适应精简[J].光学精密工程,2017,25(01):245-254.LIU Y,WANG C Y,GAO N,et al.Point cloud adaptive simplification of feature extraction[J].Optics and Precision Engineering,2017,25(01):245-254.
[12]赵夫群,周明全.颅骨点云模型的优化配准[J].光学精密工程,2017,25(07):1927-1933.ZHAO F Q,ZHOU M Q.Optimization registration of point cloud model of skull[J].Optics and Precision Engineering,2017,25(07):1927-1933.
[13]任国印,吕晓琪,杨楠,等.改进的体素生长算法在心脏局部血管提取中的应用研究[J].激光与光电子学进展,2018,5(56):1062-1069.REN G Y,LV X Q,YANG N.The improved voxels growth algorithm in cardiac local vascular extraction[J].Laser&Optoelectronics Progress,2018,5(56):1062-1069.(in Chinese)
[14]秦斌杰,庄天戈.基于体素灰度3D多模医学图像配准中的相似性测度[J].上海交通大学学报,2002,36(7):942-945.QIN B J,ZHUANG T G.Similarity measures in voxel htensity based 3Dmulti-modal medical image registration[J].Journal of Shanghai Jiaotong University,2002,36(7):942-945.(in Chinese)
[15]马建林,崔志明,张娜敏.一种新的基于区域增长的ROI分割算法[J].计算机应用研究,2008,25(5):1583-1585.MA J L,CUI Z M,WU J,et al.NovelROI segmentation method using region growing[J].Application Research of Conputers,2008,25(5):1583-1585.(in Chinese)
[16]陆剑锋,林海,潘志庚.自适应区域生长算法在医学图像分割中的应用[J].计算机辅助设计与图形学学报,2005,17(10):2168-2173.LU J F,LIN H,PAN Z G.Adaptive region growing algorithm in medical images segmentation[J].Journal of Computer-aided Design&Computer Graphics,2005,17(10):2168-2173.(in Chinese)
[17]梁思,王雷,杨晓冬.一种血管约束的局部活动轮廓模型[J].液晶与显示,2016,31(7):686-693.LIAN S,WAND L,YANG X D.A novel vessel-constrained active contour with application to vessel segmentation[J].Chinese Journal of Liquid Crystals and Displays,2016,31(7):686-693.(in Chinese)
[18]吴秋红,吴谨,朱磊,等.基于图论和FCM的图像分割算法[J].液晶与显示,2016,31(1):112-116.WU Q H,WU J,ZHU L,et al.Image segmentation algorithm based on graph theory and FCM[J].Chinese Journal of Liquid Crystals and Displays,2016,31(1):112-116.(in Chinese)
[19]吕晓琪,任国印,谷宇.心肌及心血管系统的四维可视化技术研究与实现[J].中国医学影像技术,2013,29(1):110-114.LV X Q,REN G Y,GU Y.Design and implementation of four-dimensional visualization technology for myocardium and cardiovascular system[J].Chin J Med Imaging Technol,2013,29(1):110-114.(in Chinese)
[2]韩承航,程云章.基于模糊聚类和改进C-V模型的冠状动脉图像分割方法[J].北京生物医学工程,2017,36(3):262-267.HAN C H,CHENG Y Z.Coronary image segmentation based on fuzzy clustering method and improved C-V model[J].Beijing Biomedical Engineering,2017,36(3):262-267.(in Chinese)
[3]丁蓬莉.基于深度学习的糖尿病性视网膜图像分析算法研究[D].北京:北京交通大学,2017.DING P L.Research of diabetic retinal image analysis algorithms based on deep learning[D].Beijing:beijing jiaotong university,2017.(in Chinese)
[4]徐光柱,张柳,邹耀斌,等.自适应脉冲耦合神经网络与匹配滤波器相结合的视网膜血管分割[J].光学精密工程,2017,25(03):756-764.XU G Z,ZHANG L,ZOU Y B,et al.Retinal blood segmentation with adaptive PCNN and matched filter[J].Optics and Precision Engineering,2017,25(03):756-764.
[5]仇清涛,段敬豪,巩贯忠,等.基于三维动态区域生长算法的肝脏自动分割[J].中国医学物理学杂志,2017,34(7):660-665.QIU Q T,DUAN J H,GONG G Z,et al.Liver auto-segmentation based on three-dimensional dynamic region growing algorithm[J].Chinese Journal of Medical Physics,2017,34(7):660-665.(in Chinese)
[6]钱峰,马秀丽,杨胜齐,等.移动立方体算法的研究和改进[J].计算机工程与应用,2010,46(34):177-180.QIAN F,MA X L,YANG S Q,et al.Research and improvement of marching cubes algorithm[J].Computer Engineering and Applications,2010,46(34):177-180.(in Chinese)
[7]JF O'Brien,NF Ezquerra.Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal,spatial,and structural constraints[J].Visualization in Biomedical Computing,1994,14(2):657-662.
[8]SCHMITT H,GRASS M,RASCHE V,et al.An X-ray-based method for the determination of the contrast agent propagation in 3-D vessel structures[J].IEEE Transactions on Medical Imaging,1996,15(3):377-379.
[9]WANG S,FU Y,YUE Y,et al.Fast and Automatic Segmentation of Ascending Aorta in MSCT Volume Data[J].International Congress on Image&Signal Processing,2009,45(10):1-5.
[10]HUANG P,WANG C C L.Volume and complexity bounded simplification of solid model represented by binary space partition[J].Acm Symposium on Solid&Physical Modeling,2010,12(4):1158-1162.
[11]刘迎,王朝阳,高楠,张宗华.特征提取的点云自适应精简[J].光学精密工程,2017,25(01):245-254.LIU Y,WANG C Y,GAO N,et al.Point cloud adaptive simplification of feature extraction[J].Optics and Precision Engineering,2017,25(01):245-254.
[12]赵夫群,周明全.颅骨点云模型的优化配准[J].光学精密工程,2017,25(07):1927-1933.ZHAO F Q,ZHOU M Q.Optimization registration of point cloud model of skull[J].Optics and Precision Engineering,2017,25(07):1927-1933.
[13]任国印,吕晓琪,杨楠,等.改进的体素生长算法在心脏局部血管提取中的应用研究[J].激光与光电子学进展,2018,5(56):1062-1069.REN G Y,LV X Q,YANG N.The improved voxels growth algorithm in cardiac local vascular extraction[J].Laser&Optoelectronics Progress,2018,5(56):1062-1069.(in Chinese)
[14]秦斌杰,庄天戈.基于体素灰度3D多模医学图像配准中的相似性测度[J].上海交通大学学报,2002,36(7):942-945.QIN B J,ZHUANG T G.Similarity measures in voxel htensity based 3Dmulti-modal medical image registration[J].Journal of Shanghai Jiaotong University,2002,36(7):942-945.(in Chinese)
[15]马建林,崔志明,张娜敏.一种新的基于区域增长的ROI分割算法[J].计算机应用研究,2008,25(5):1583-1585.MA J L,CUI Z M,WU J,et al.NovelROI segmentation method using region growing[J].Application Research of Conputers,2008,25(5):1583-1585.(in Chinese)
[16]陆剑锋,林海,潘志庚.自适应区域生长算法在医学图像分割中的应用[J].计算机辅助设计与图形学学报,2005,17(10):2168-2173.LU J F,LIN H,PAN Z G.Adaptive region growing algorithm in medical images segmentation[J].Journal of Computer-aided Design&Computer Graphics,2005,17(10):2168-2173.(in Chinese)
[17]梁思,王雷,杨晓冬.一种血管约束的局部活动轮廓模型[J].液晶与显示,2016,31(7):686-693.LIAN S,WAND L,YANG X D.A novel vessel-constrained active contour with application to vessel segmentation[J].Chinese Journal of Liquid Crystals and Displays,2016,31(7):686-693.(in Chinese)
[18]吴秋红,吴谨,朱磊,等.基于图论和FCM的图像分割算法[J].液晶与显示,2016,31(1):112-116.WU Q H,WU J,ZHU L,et al.Image segmentation algorithm based on graph theory and FCM[J].Chinese Journal of Liquid Crystals and Displays,2016,31(1):112-116.(in Chinese)
[19]吕晓琪,任国印,谷宇.心肌及心血管系统的四维可视化技术研究与实现[J].中国医学影像技术,2013,29(1):110-114.LV X Q,REN G Y,GU Y.Design and implementation of four-dimensional visualization technology for myocardium and cardiovascular system[J].Chin J Med Imaging Technol,2013,29(1):110-114.(in Chinese)