Separation and Visualization of Arteries and Heart in 3D Computed Tomography Angiography Images

doi:10.1007/s12204-017-1792-x

Abstract

Abstract:

The visualization of arteries and heart usually plays a crucial role in the clinical diagnosis, but researchers face the problems of region selection and mutual occlusion in clinical visualization. Therefore, the arteries and the heart cannot be easily visualized by current visualization methods. To solve the problems, we propose a new framework for arteries and cardiac visualization by combining a priori knowledge and the set operations. Firstly, a suitable region can be easily determined in the transfer function space with a priori knowledge and the visual feedback results. Secondly, the arteries and the heart can be directly extracted by the marked seed point. Finally, the arteries and the heart are separated for solving mutual occlusion through the set operations. This framework can easily solve the mutual occlusion problem in clinical visualization and greatly improve the region selection method in the transfer function space. Its effectiveness has been demonstrated on the basis of many experimental results.

Key words:

occlusion removal| multi-atlas| interactive extraction| set operations| volume rendering

摘要：

关键词:

occlusion removal| multi-atlas| interactive extraction| set operations| volume rendering

CLC Number:

TP 391.9

LAN Shouren (兰守忍), CUI Chaoyi (崔超毅), LIU Xin (刘鑫), JOHNSON Shane,SU Jialiang (苏佳良), CHEN Benzhi (陈本智), WANG Lisheng* (王利生). Separation and Visualization of Arteries and Heart in 3D Computed Tomography Angiography Images[J]. Journal of shanghai Jiaotong University (Science), 2017, 22(1): 1-009.

References 31

[1]	LEVOY M. Display of surfaces from volume data [J].IEEE Computer graphics and Applications, 1988, 8(3):29-37.
[2]	DREBIN R A, CARPENTER L, HANRAHAN P. Volumerendering [J]. Computer Graphics, 1988, 22(4):65-74.
[3]	RUBIN G D, BEAULIEU C F, ARGIRO V, et al. Perspectivevolume rendering of CT and MR images: Applicationsfor endoscopic imaging [J]. Radiology, 1996,199(2): 321-330.
[4]	JOHNSON P T, HEATH D G, KUSZYK B S, et al. CTangiography with volume rendering: Advantages andapplications in splanchnic vascular imaging [J]. Radiology,1996, 200(2): 564-568.
[5]	KINDLMANN G, DURKIN JW. Semi-automatic generationof transfer functions for direct volume rendering[C]//Proceedings of the 1998 IEEE Symposium onVolume Visualization. [s. l.]: IEEE, 1998: 79-86.
[6]	SATO Y, WESTIN C F, BHALERAO A, et al. Tissueclassification based on 3D local intensity structures forvolume rendering [J]. IEEE Transactions on Visualizationand Computer Graphics, 2000, 6(2): 160-180.
[7]	PFISTER H, LORENSEN B, BAJAJ C, et al. Thetransfer function bake-off [J]. IEEE Computer Graphicsand Applications, 2001, 21(3): 16-22.
[8]	KNISS J, KINDLMANN G, HANSEN C. Multidimensionaltransfer functions for interactive volume rendering[J]. IEEE Transactions on Visualization and ComputerGraphics, 2002, 8(3): 270-285.
[9]	ˇSEREDA P, BARTROL′I A V, SERLIE I W O, et al.Visualization of boundaries in volumetric data sets usingLH histograms [J]. IEEE Transactions on Visualizationand Computer Graphics, 2006, 12(2): 208-218.
[10]	WU Y C, QU H M. Interactive transfer function designbased on editing direct volume rendered images[J]. IEEE Transactions on Visualization and ComputerGraphics, 2007, 13(5): 1027-1040.
[11]	WANG L, ZHAO X, KAUFMAN A E. Modified dendrogramof attribute space for multidimensional transferfunction design [J]. IEEE Transactions on Visualizationand Computer Graphics, 2012, 18(1): 121-131.
[12]	CABAN J J, RHEINGANS P. Texture-based transferfunctions for direct volume rendering [J]. IEEETransactions on Visualization and Computer Graphics,2008, 14(6): 1364-1371.
[13]	KINDLMANN G, WHITAKER R, TASDIZEN T,et al. Curvature-based transfer functions for directvolume rendering: Methods and applications[C]//Proceedings of Visualization 2003. Seattle, USA:IEEE, 2003: 513-520.
[14]	ANDERSON C M, SALONER D, TSURUDA J S,et al. Artifacts in maximum-intensity-projection displayof MR angiograms [J]. American Journal ofRoentgenology, 1990, 154(3): 623-629.
[15]	SATO Y, SHIRAGA N, NAKAJIMA S, et al. Localmaximum intensity projection (LMIP): A new renderingmethod for vascular visualization [J]. Journal ofComputer Assisted Tomography, 1998, 22(6): 912-917.
[16]	CORREA C D, MA K L. Size-based transfer functions:A new volume exploration technique [J]. IEEETransactions on Visualization and Computer Graphics,2008, 14(6): 1380-1387.
[17]	JOSHI A, QIAN X N, DIONE D P, et al. Effective visualizationof complex vascular structures using a nonparametricvessel detection method [J]. IEEE Transactionson Visualization and Computer Graphics, 2008,14(6): 1603-1610.
[18]	PRA?NI J S, ROPINSKI T, MENSMANN J, et al.Shape-based transfer functions for volume visualization[C]//IEEE Pacific Visualization Symposium 2010.Taipei, China: IEEE, 2010: 9-16.
[19]	L¨ATH′EN G, LINDHOLM S, LENZ R, et al. Automatictuning of spatially varying transfer functions forblood vessel visualization [J]. IEEE Transactions onVisualization and Computer Graphics, 2012, 18(12):2345-2354.
[20]	SELVER M A, G¨UZELIS C. Semiautomatic transferfunction initialization for abdominal visualization usingself-generating hierarchical radial basis functionnetworks [J]. IEEE Transactions on Visualization andComputer Graphics, 2009, 15(3): 395-409.
[21]	LUNDSTR¨OM C, LJUNG P, YNNERMAN A. Localhistograms for design of transfer functions in direct volumerendering [J]. IEEE Transactions on Visualizationand Computer Graphics, 2006, 12(6): 1570-1579.
[22]	ZHANG Q, EAGLESON R, PETERS T M. Real-timevisualization of 4D cardiac MR images using graphicsprocessing units [C]//3rd IEEE International Symposiumon Biomedical Imaging. [s. l.]: IEEE, 2006: 343-346.
[23]	SERA T, YOKOTA H, FUJISAKI K, et al. Developmentof high-resolution 4D in vivo-CT for visualizationof cardiac and respiratory deformations of smallanimals [J]. Physics in Medicine and Biology, 2008,53(16): 4285-4301.
[24]	LAIDLAW D H, FLEISCHER K W, BARR A H.Partial-volume Bayesian classification of material mixturesin MR volume data using voxel histograms [J].IEEE Transactions on Medical Imaging, 1998, 17(1):74-86.
[25]	LAN S R, WANG L S, SONG Y P, et al. Improvingseparability of structures with similar attributesin 2D transfer function design [J]. IEEE Transactionson Visualization and Computer Graphics, 2016. Doi:10.1109/TVCG.2016.2537341 (published online).
[26]	ROETTGER S, BAUER M, STAMMINGER M. Spatializedtransfer functions [C]// EUROGRAPHICSIEEEVGTC Symposium on Visualization. [s. l.]:IEEE, 2005: 271-278.
[27]	HAHN H K, PREIM B, SELLE D, et al. Visualizationand interaction techniques for the exploration ofvascular structures [C]// Proceedings of Visualization2001. San Diego, USA: IEEE, 2001: 395-402.
[28]	PREIM B, OELTZE S. 3D visualization of vasculature:An overview [C]//Visualization in Medicine and LifeSciences. Berlin Heidelberg: Springer-Verlag, 2008:39-59.
[29]	WANG Y H, ZHANG J, LEHMANN D J, et al.Automating transfer function design with valley cellbasedclustering of 2D density plots [C]//EurographicsConference on Visualization. Oxford, UK:Wiley, 2012:1295-1304.
[30]	IP C Y, VARSHNEY A, JAJA J. Hierarchical explorationof volumes using multilevel segmentation ofthe intensity-gradient histograms [J]. IEEE Transactionson Visualization and Computer Graphics, 2012,18(12): 2355-2363.
[31]	NING H, YANG R Q, MA A M, et al. Interactive 3Dmedical data cutting using closed curve with arbitraryshape [J]. Computerized Medical Imaging and Graphics,2015, 40: 120-127.