|本期目录/Table of Contents|

[1]杨志灵,吴祯祥,黄绍辉.一种基于ITK和VTK的肝脏管道自动分级算法[J].厦门大学学报(自然科学版),2013,52(2):190.[doi:10.6043/j.issn.0438-0479.2013.02.010]
 YANG Zhi-ling,WU Zhen-xiang,HUANG Shao-hui*.An Automatic Hepatic Vessel Classification Algorithm Based on ITK and VTK[J].Journal of Xiamen University(Natural Science),2013,52(2):190.[doi:10.6043/j.issn.0438-0479.2013.02.010]
点击复制

一种基于ITK和VTK的肝脏管道自动分级算法(PDF)
分享到:

《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
52卷
期数:
2013年第2期
页码:
190
栏目:
研究论文
出版日期:
2013-05-01

文章信息/Info

Title:
An Automatic Hepatic Vessel Classification Algorithm Based on ITK and VTK
作者:
杨志灵吴祯祥黄绍辉
厦门大学信息科学与技术学院,福建厦门361005
Author(s):
YANG Zhi-lingWU Zhen-xiangHUANG Shao-hui*
School of Information Science and Engineering,Xiamen University,Xiamen 361005,China
关键词:
肝脏管道管道分级肝脏分支类型
Keywords:
hepatic vesselvessel classificationhepatic branch type
分类号:
TP 391
DOI:
10.6043/j.issn.0438-0479.2013.02.010
文献标志码:
-
摘要:
肝脏管道的分级是肝脏管道分支类型判定、管道变异情况分析的重要步骤,是针对性地制定个性化手术方案,从而降低肝切除手术风险的前提.基于ITK(insight segmentation and registration toolkit)和VTK(visualization toolkit)设计了一种肝脏管道自动分级的算法,实现了肝脏CT序列的管道自动提取、管道细化、管道拓扑建模以及管道分级的功能,最后用不同的颜色将不同级别的管道进行三维显示,便于医生更直观地对肝脏的管道系统的形态及结构进行分析,为制定术前手术计划提供帮助.
Abstract:
The hepatic vessel classification is an important step in the process of the hepatic vessel branch type determination,and vessel variation analysis.It is also the premise of targeting to develop a personalized surgery program with reducing the risk of hepatic resection.In this paper,an automatic hepatic vessel classification algorithm using insight segmentation and registration toolkit(ITK) and visualization toolkit(VTK) is introduced.The algorithm implements the liver CT sequence of vessel automatic extraction,vessel thinning,vessel topology modeling,vessel classification,and the different levels of vessel coloring display in 3D aiming at facilitating the doctor much intuitively with hepatic duct system form and structure analysis to make the preoperative operation plan.

参考文献/References:


[1]朱明德,方驰华.肝脏管道系统变异在活体肝移植中的意义[J].肝胆外科杂志,2005,13(6):473-475.
[2]Zhang Y J,Gerbrands J J.Transition region determination based thresholding[J].Pattern Recognition Letter,1991,12:13-23.
[3]Sahoo P,Wilkinsand C,Yeager J.Threshold selection using Renyi′s entropy[J].Pattern Recognition,1997,30(1):71-84.
[4]Manousakas I N,Undrill P E,Cameron G G,et al.SPlit-and-merge segmentation of magnetic resonance medical images:performance evaluation and extension to three dimensions[J].ComPuters and Biomedical Research,1998,31:393-412.
[5]Kass M,Witkin A,TerzoPoulos D.Snakes-active contour models[J].International Journal of Computer Vision,1987,1(4):321-331.
[6]Osher S,Sethian J.Fronts propagating with curvature dependent speed[J].J Comput Phys,1988,79:12-49.
[7]Sethian J A.Fast marching methods[J].SIAM Rev,1999,41:199-235.
[8]Falcao A X,Udupa J K,Samarasekera S,et al.User-steered image segmentation paradigms:live wire and live lane[J].Graphic Models and Image Processing,1998,60:233-260.
[9]周振环,王安明,王京阳,等.医学图像分割与配准[M].成都:电子科技大学出版社,2007.
[10]Kapur J N,Sahoo P K,Wong A K C.A new method for gray level picture thresholding using the entropy of the histogram[J].Computing Vision Graphics Image Process,1985,29(3):273-285.
[11]Chang F J,Yen J C,Chang S.A new criterion for automatic multilevel thresholding[J].IEEE Trans Image Process,1995,4(3):370-378.
[12]Lee T C,Kashya R L.Building skeleton model via 3-D medial surface/axis thinning algorithms[J].VCGIP:Graph Models Image Process,1994,56(6):462-478.
[13]Palagyi K,Kuba A.A parallel 12-subiteration 3D thinning algorithm to extract medial lines[J].Computer Analysis of Images and Patterns,1977,1296:400-407.
[14]Palagyi K,Kuba A.A 3D 6-subiteration thinning algorithm for extracting medial lines[J].Pattern Recognition Letters,1998,19(7):613-627.
[15]Palagyi K,Balogh E,Kuba A,et al.A sequential 3D thinning algorithm and its medical applications[J].Lecture Notes in Computer Science,2001,2082:409-415.
[16]Saha P K,Chaudhuri B B.Detection of 3-D simple points for topology preserving transformations with application to thinning[J].IEEE Trnsactions on Pattern Analysis and Machine Intelligence,1994(16):1028-1032.
[17]Ma C M,Aonka M.A fully paralled 3D thinning algorithm and its applications[J].Computer Vision and Image Understanding,1996,64(3):420-433.
[18]Ma C M.Connectivity preservation of 3D 6-subiteration thinning algorithms[J].Graphical Models and Image Processing,1966,58(4):382-386.
[19]Kirabas C,Quek F K H.Vessel extraction in medical by 3D wave propagation and traceback[C]//IEEE Conference Bio-Infor-matics and Bio-Engineering(BIBE).[S.l.]:IEEE,2003:174-181.
[20]Thomas D,Laurent D C.Fast extraction of minimal path in 3D images and application to virtual endoscopy[J].Medical Image Analysis,2001(4):281-299.
[21]Au S,Hamid S I,Reza A I.Fast skeletonization algorithm for 3D elongated objects[C]∥Proceedings of SHE Medical Imaging.San Diego,SPIE,2001:323-330.
[22]陈磊,王胜军,郑全录,等.基于CT图像的三维拓扑细化算法及其在心脏CAD中的应用[J].计算机应用,2007,27(B06):406-410.
[23]滕奇志,康瑕,唐棠,等.基于升序复核的并行三维图像骨架化算法[J].光学精密工程,2009,17(10):2528-2532.
[24]Bouix S,Siddiqi K,Tannenbaum A.Flux driven automatic centerline extraction[R].Montreal:McGill University,2004.
[25]Siddiqi K,Kimia B B,Shu C.Geometric shock-capturing eno schemes for sub-pixel Interpolation computation and curve evolutiion[J].Graphical Models and Image Processing,1997,59(5):278-301.

备注/Memo

备注/Memo:
收稿日期:2012-11-21
基金项目:国家自然科学基金青年科学基金项目(61001144) *通信作者:hsh@xmu.edu.cn
更新日期/Last Update: 2013-03-20