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Issue No.03 - March (2013 vol.19)
pp: 353-366
R. Wiemker , Philips Res. Lab. Hamburg, Hamburg, Germany
T. Klinder , Philips Res. Lab. Hamburg, Hamburg, Germany
M. Bergtholdt , Philips Res. Lab. Hamburg, Hamburg, Germany
K. Meetz , Philips Res. Lab. Hamburg, Hamburg, Germany
I. C. Carlsen , Philips Res. Lab. Hamburg, Hamburg, Germany
T. Bülow , Philips Res. Lab. Hamburg, Hamburg, Germany
ABSTRACT
The concept of curvature and shape-based rendering is beneficial for medical visualization of CT and MRI image volumes. Color-coding of local shape properties derived from the analysis of the local Hessian can implicitly highlight tubular structures such as vessels and airways, and guide the attention to potentially malignant nodular structures such as tumors, enlarged lymph nodes, or aneurysms. For some clinical applications, however, the evaluation of the Hessian matrix does not yield satisfactory renderings, in particular for hollow structures such as airways, and densely embedded low contrast structures such as lymph nodes. Therefore, as a complement to Hessian-based shape-encoding rendering, this paper introduces a combination of an efficient sparse radial gradient sampling scheme in conjunction with a novel representation, the radial structure tensor (RST). As an extension of the well-known general structure tensor, which has only positive definite eigenvalues, the radial structure tensor correlates position and direction of the gradient vectors in a local neighborhood, and thus yields positive and negative eigenvalues which can be used to discriminate between different shapes. As Hessian-based rendering, also RST-based rendering is ideally suited for GPU implementation. Feedback from clinicians indicates that shape-encoding rendering can be an effective image navigation tool to aid diagnostic workflow and quality assurance.
INDEX TERMS
Rendering (computer graphics), Tensile stress, Eigenvalues and eigenfunctions, Image color analysis, Biomedical imaging,tumors, Curvature-based rendering, shape-based rendering, vessels, airways, lymph nodes
CITATION
R. Wiemker, T. Klinder, M. Bergtholdt, K. Meetz, I. C. Carlsen, T. Bülow, "A Radial Structure Tensor and Its Use for Shape-Encoding Medical Visualization of Tubular and Nodular Structures", IEEE Transactions on Visualization & Computer Graphics, vol.19, no. 3, pp. 353-366, March 2013, doi:10.1109/TVCG.2012.136
REFERENCES
[1] C. Lundstrom, P. Ljung, and A. Ynnerman, "Local Histograms for Design of Transfer Functions in Direct Volume Rendering," IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 6, pp. 1570-1579, Nov./Dec. 2006.
[2] D. Patel, M. Haidacher, J.P. Balabanian, and E.M. Groller, "Moment Curves," Proc. IEEE Visualization Symp. (PacificVis' 09), pp. 201-208, 2009.
[3] S. Lindholm, P. Ljung, C. Lundstrom, A. Persson, and A. Ynnerman, "Spatial Conditioning of Transfer Functions Using Local Material Distributions," IEEE Trans. Visualization and Computer Graphics, vol. 16, no. 6, pp. 1301-1310, Nov./Dec. 2010.
[4] G. Kindlmann and J.W. Durkin, "Semi-Automatic Generation of Transfer Functions for Direct Volume Rendering," Proc. IEEE Symp. Vol. Visualization, vol. 98, pp. 79-86, 1998.
[5] V. Pekar, R. Wiemker, and D. Hempel, "Fast Detection of Meaningful Isosurfaces for Volume Data Visualization," Proc. IEEE Visualization Conf. (VIS '01), p. 230, 2001.
[6] Y. Sato, C.F. Westin, A. Bhalerao, S. Nakajima, N. Shiraga, S. Tamura, and R. Kikinis, "Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering," IEEE Trans. Visualization and Computer Graphics, vol. 6, no. 2, pp. 160-180, Apr.-June 2000.
[7] J. Hladuvka, A. König, and E. Gröller, "Curvature-Based Transfer Functions for Direct Volume Rendering," Proc. Spring Conf. Computer Graphics, vol. 16, pp. 58-65, 2000.
[8] G. Kindlmann, R. Whitaker, T. Tasdizen, and T. Möller, "Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications," Proc. IEEE Visualization Conf. (VIS '03), p. 67, 2003.
[9] S. Peled, H. Gudbjartsson, C.F. Westin, R. Kikinis, and F.A. Jolesz, "Magnetic Resonance Imaging Shows Orientation and Asymmetry of White Matter Fiber Tracts," Brain Research, vol. 780, no. 1, pp. 27-33, 1998.
[10] A. Vilanova, S. Zhang, G. Kindlmann, and D. Laidlaw, "An Introduction to Visualization of Diffusion Tensor Imaging and Its Applications," Visualization and Processing of Tensor Fields, J. Weickert, ed., pp. 121-153, Springer, 2006.
[11] R. Wiemker, E. Dharaiya, A. Steinberg, T. Bülow, A. Saalbach, and T. Vik, "Filter Learning and Evaluation of the Computer Aided Visualization and Analysis (CAVA) Paradigm for Pulmonary Nodules Using the LIDC-IDRI Database," Proc. SPIE Medical Imaging, vol. 7624, 2010.
[12] R. Wiemker, E.D. Dharaiya, and T. Bülow, "Hesse Rendering for Computer-Aided Visualization and Analysis of Anomalies at Chest CT and Breast MR Imaging," Radiographics, vol. 32, no. 1, pp. 289-304, Feb. 2012.
[13] H. Yoshida, Y. Masutani, P. MacEneaney, D.T. Rubin, and A.H. Dachman, "Computerized Detection of Colonic Polyps at CT Colonography on the Basis of Volumetric Features: Pilot Study," Radiology, vol. 222, no. 2, pp. 327-336, 2002.
[14] J.S. Praßni, J. Mensmann, T. Ropinski, and K. Hinrichs, "Shape-Based Transfer Functions for Volume Visualization," Proc. IEEE Pacific Visualization (PacificVis '10), pp. 9-16, 2010.
[15] T. Bülow, R. Wiemker, L. Arbash Meinel, J. Buurman, H. Abe, and G. Newstead, "Towards CAVA: Lymph Node Enhanced Visualization for Fast Visual Detection of Axillary Lymph Nodes in Breast MR Images," Proc. Conf. Computer Assisted Radiology and Surgery (CARS '09), 2009.
[16] H. Knutsson, "Representing Local Structure Using Tensors," Proc. Sixth Scandinavian Conf. Image Analysis, pp. 244-251, 1989.
[17] B. Jähne and H. Haußecker, Computer Vision and Applications. Academic Press, 2000.
[18] W. Förstner and E. Gülch, "A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centers of Circular Features," Proc. ISPRS Intercommission Conf. Fast Processing of Photogrammetric Data, pp. 281-305, 1987.
[19] J. Bigün and G.H. Granlund, "Optimal Orientation Detection of Linear Symmetry," Proc. First Int'l Conf. Computer Vision, pp. 433-438, 1987.
[20] T. Brox, J. Weickert, B. Burgeth, and P. Mrázek, "Nonlinear Structure Tensors," Image and Vision Computing, vol. 24, no. 1, pp. 41-55, 2006.
[21] S. Di Zenzo, "A Note on the Gradient of a Multi-Image," Computer Vision, Graphics, and Image Processing, vol. 33, no. 1, pp. 116-125, 1986.
[22] J. Bigun, T. Bigun, and K. Nilsson, "Recognition by Symmetry Derivatives and the Generalized Structure Tensor," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp. 1590-1605, Dec. 2004.
[23] T. Schultz, J. Weickert, and H.-P. Seidel, "A Higher-Order Structure Tensor," Visualization and Processing of Tensor Fields —Advances and Perspectives, pp. 263-280, Springer, 2009.
[24] C. Xiao, M. Staring, D. Shamonin, J.H.C. Reiber, J. Stolk, and B.C. Stoel, "A Strain Energy Filter for 3D Vessel Enhancement with Application to Pulmonary CT Images," Medical Image Analysis, vol. 15, pp. 112-124, 2011.
[25] Q. Li, S. Sone, and K. Doi, "Selective Enhancement Filters for Nodules, Vessels, and Airway Walls in Two- and Three-Dimensional CT Scans," Medical Physics, vol. 30, no. 8, pp. 2040-2051, 2003.
[26] C. Lorenz, I. Carlsen, T. Buzug, C. Fassnacht, and J. Weese, "Multi-Scale Line Segmentation with Automatic Estimation of Width, Contrast and Tangential Direction in 2D and 3D Medical Images," Proc. First Joint Conf. Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery, J. Troccaz, E. Grimson, and R. Mösges, eds., pp. 233-242, 1997.
[27] A. Frangi, W. Niessen, K. Vincken, and M. Viergever, "Multiscale Vessel Enhancement Filtering," Proc. Conf. Medical Image Computing and Computer-Assisted Interventation (MICCAI '98), pp. 130-137, 1998.
[28] K. Krissian, G. Malandain, and N. Ayache, "Model-Based Detection of Tubular Structures in 3D Images," Computer Vision and Image Understanding, vol. 80, pp. 130-171, 2000.
[29] P. Mendonça, R. Bhotika, F. Zhao, and J. Miller, "Lung Nodule Detection via Bayesian Voxel Labeling," Proc. Int'l Conf. Information Processing in Medical Imaging, pp. 134-146, 2007.
[30] T. Pock, R. Beichel, and H. Bischof, "A Novel Robust Tube Detection Filter for 3D Centerline Extraction," Proc. 14th Scandinavian Conf. Image Analysis (SCIA '05), H. Kalviainen, J. Parkkinen, and A. Kaarna, eds., pp. 481-490, 2005.
[31] G. Agam, S.G. Armato, and C. Wu, "Vessel Tree Reconstruction in Thoracic CT Scans with Application to Nodule Detection," IEEE Trans. Medical Imaging, vol. 24, no. 4, pp. 486-499, Apr. 2005.
[32] A. Joshi, X. Qian, D.P. Dione, K.R. Bulsara, C.K. Breuer, A.J. Sinusas, and X. Papademetris, "Effective Visualization of Complex Vascular Structures Using a Non-Parametric Vessel Detection Method," IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, pp. 1603-1610, Nov. 2008.
[33] X. Qian, M.P. Brennan, D.P. Dione, W.L. Dobrucki, M.P. Jackowski, C.K. Breuer, A.J. Sinusas, and X. Papademetris, "A Non-Parametric Vessel Detection Method for Complex Vascular Structures," Medical Image Analysis, vol. 13, no. 1, pp. 49-61, 2009.
[34] T. Lindeberg, "Scale-Space Theory: A Basic Tool for Analysing Structures at Different Scales," J. Applied Statistics, vol. 21, no. 2, pp. 225-270, 1994.
[35] J. Weickert, Anisotropic Diffusion in Image Processing. Teubner, 1998.
[36] A. Kaufman and K. Mueller, "Overview of Volume Rendering," Visualization Handbook, C.D. Hansen and C.R. Johnson, eds., pp. 127-174, Elsevier, 2004.
[37] D. Jones, M. Horsfield, and A. Simmons, "Optimal Strategies for Measuring Diffusion in Anisotropic Systems by Magnetic Resonance Imaging," Magnetic Resonance Medicine, vol. 42, pp. 515-525, 1999.
[38] C.D. Hansen and C.R. Johnson, The Visualization Handbook. Elsevier, 2004.
[39] P. Peloschek, J. Sailer, M. Weber, C.J. Herold, M. Prokop, and C. Schaefer-Prokop, "Pulmonary Nodules: Sensitivity of Maximum Intensity Projection versus that of Volume Rendering of 3D Multidetector CT Data," Radiology, vol. 243, no. 2, pp. 561-569, 2007.
[40] R. Wiemker, "Aspects of Computer-Aided Detection (CAD) and Volumetry of Pulmonary Nodules Using Multislice CT," British J. Radiology, vol. 78, no. suppl_1, pp. S46-S56, Jan. 2005.
[41] P.R.S. Mendonça, R. Bhotika, S.A. Sirohey, W.D. Turner, J.V. Miller, and R.S. Avila, "Model-Based Analysis of Local Shape for Lesion Detection in CT Scans," Proc. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI '05), pp. 688-695, 2005.
[42] C.I. Henschke, P. Boffetta, O. Gorlova, R. Yip, J.O. DeLancey, and M. Foy, "Assessment of Lung-Cancer Mortality Reduction from CT Screening," Lung Cancer, vol. 71, no. 3, pp. 328-332, 2011.
[43] Y.M.T.A. van Durme, K.M.C. Verhamme, T. Stijnen, F.J.A. van Rooij, G.R. Van Pottelberge, A. Hofman, G.F. Joos, B.H.C. Stricker, and G.G. Brusselle, "Prevalence, Incidence, and Lifetime Risk for the Development of COPD in the Elderly: The Rotterdam Study," Chest, vol. 135, no. 2, pp. 368-377, Feb. 2009.
[44] P. Lo, B. van Ginneken, J. Reinhardt, and M. de Bruijne, "Extraction of Airways from CT (EXACT '09)," Proc. Second Int'l Workshop Pulmonary Image Analysis, pp. 175-189, 2009.
[45] K. Doi, "Current Status and Future Potential of Computer-Aided Diagnosis in Medical Imaging," British J. Radiology, vol. 78, no. suppl_1, pp. S3-S19, Jan. 2005.
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