This Article 
 Bibliographic References 
 Add to: 
Shape “Break-and-Repair” Strategy and Its Application to Automated Medical Image Segmentation
January 2011 (vol. 17 no. 1)
pp. 115-124
Jiantao Pu, University of Pittsburgh, Pittsburgh, PA
David S. Paik, Stanford University, Stanford, CA
Xin Meng, University of Pittsburgh, Pittsburgh, PA
Justus E. Roos, Stanford University, Stanford, CA
Geoffrey D. Rubin, Stanford University, Stanford, CA
In three-dimensional medical imaging, segmentation of specific anatomy structure is often a preprocessing step for computer-aided detection/diagnosis (CAD) purposes, and its performance has a significant impact on diagnosis of diseases as well as objective quantitative assessment of therapeutic efficacy. However, the existence of various diseases, image noise or artifacts, and individual anatomical variety generally impose a challenge for accurate segmentation of specific structures. To address these problems, a shape analysis strategy termed “break-and-repair” is presented in this study to facilitate automated medical image segmentation. Similar to surface approximation using a limited number of control points, the basic idea is to remove problematic regions and then estimate a smooth and complete surface shape by representing the remaining regions with high fidelity as an implicit function. The innovation of this shape analysis strategy is the capability of solving challenging medical image segmentation problems in a unified framework, regardless of the variability of anatomical structures in question. In our implementation, principal curvature analysis is used to identify and remove the problematic regions and radial basis function (RBF) based implicit surface fitting is used to achieve a closed (or complete) surface boundary. The feasibility and performance of this strategy are demonstrated by applying it to automated segmentation of two completely different anatomical structures depicted on CT examinations, namely human lungs and pulmonary nodules. Our quantitative experiments on a large number of clinical CT examinations collected from different sources demonstrate the accuracy, robustness, and generality of the shape “break-and-repair” strategy in medical image segmentation.

[1] S.G. Armato III, M.I. Giger, C.J. Moran, J.T. Blackburn, K. Doi, and H. MacMabon, "Computerized Detection of Pulmonary Nodules on CT Scans," Radiographics, vol. 19, pp. 1303-1311, 1999.
[2] S. Hu, E.A. Hoffman, and J.M. Reinhardt, "Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images," IEEE Trans. Medical Imaging, vol. 20, no. 6, pp. 490-498, June 2001.
[3] I. Sluimer, A. Schilham, M. Prokop, and B. van Ginneken, "Computer Analysis of Computed Tomography Scans of the Pulmonary: A Survey," IEEE Trans. Medical Imaging, vol. 25, no. 4, pp. 385-405, Apr. 2006.
[4] D. Bartz, D. Mayer, J. Fischer, S. Ley, A. del Rio, S. Thust, C. Heussel, H. Kauczor, and W. Straßer, "Hybrid Segmentation and Exploration of The Human Lungs," Proc. IEEE Visualization Conf., pp. 177-184, 2003.
[5] R. Garnavi, A. Baraani-Dastjerdi, H.A. Moghaddam, M. Giti, and A.A. Rad, "A New Segmentation of Lung HRCT Images," Proc. Digital Image Computing on Techniques and Applications, pp. 52-59, 2005.
[6] A.M. Ali and A.A. Farag, "Automatic Lung Segmentation of Volumetric Low-Dose CT Scans Using Graph Cuts," Lecture Notes in Computer Science, vol. 5358, pp. 258-267, Springer, 2008.
[7] H.S. Kim, H. Yoon, K.N. Trung, and G.S. Lee, "Automatic Lung Segmentation in CT Images Using Anisotropic Diffusion and Morphology Operation," Proc. Seventh IEEE Int'l Conf. Computer and Information Technology, pp. 557-561, 2007.
[8] M.N. Prasad, M.S. Brown, S. Ahmad, F. Abtin, J. Allen, I. da Costa, H.J. Kim, M.F. McNitt-Gray, J.G. Goldin, and S.K. Warfield, "Automatic Segmentation of Lung Parenchyma in the Presence of Diseases Based on Curvature of Ribs," Academic Radiology, vol. 15, no. 9, pp. 1173-80, 2008.
[9] Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, "Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template-Matching Technique," IEEE Trans. Medical Imaging, vol. 20, no. 7, pp. 595-604, July 2001.
[10] G.D. Rubin, J. Lyo, D.S. Paik, A. Sherbondy, L. Chow, A.N. Leung, R. Mindelzun, S.E. Zinck, D.P. Naidich, and S. Napel, "Pulmonary Nodules in MDCT Scans: Impact of Computer-Aided Detection," Radiology, vol. 235, pp. 274-283, 2005.
[11] L. Zhang, E.A. Hoffman, and J.M. Reinhardt, "Atlas-Driven Pulmonary Lobe Segmentation in Volumetric X-Ray CT Images," IEEE Trans. Medical Imaging, vol. 25, no. 1, pp. 1-16, Jan. 2006.
[12] J. Wang, M. Betke, and J.P. Ko, "Pulmonary Fissure Segmentation on CT," Medical Image Analysis, vol. 10, pp. 530-547, 2006.
[13] J. Wang, F. Li, and Q. Li, "Automated Segmentation of Lungs with Severe Interstitial Lung Disease in CT," Medical Physics, vol. 36, no. 10, pp. 4952-4599, 2009.
[14] W. Park, E. Hoffman, and M. Sonka, "Segmentation of Intrathoracic Airway Trees: A Fuzzy Logic Approach," IEEE Trans. Medical Imaging, vol. 17, no. 4, pp. 489-497, Aug. 1998.
[15] Y. Masutani, "RBF-Based Representation of Volumetric Data: Application in Visualization and Segmentation," Proc. Medical Image Computing and Computer-Assisted Intervention (MICCAI '02), pp. 300-307, 2002.
[16] Cancer Facts and Figures 2009, Am. Cancer Soc., 2009.
[17] A. Reeves, A. Chan, D. Yankelevitz, C. Henschke, B. Kressler, and W. Kostis, "On Measuring the Change in Size of Pulmonary Nodules," IEEE Trans. Medical Imaging, vol. 25, no. 4, pp. 435-450, Apr. 2006.
[18] I. Petkovska, M.S. Brown, J.G. Goldin, H.J. Kim, M.F. McNitt-Gray, F.G. Abtin, R.J. Ghurabi, and D.R. Aberle, "The Effect of Lung Volume on Nodule Size on CT," Academic Radiology, vol. 14, no. 4, pp. 476-485, 2007.
[19] Y. Kawata, N. Niki, H. Ohmatsu, K. Eguchi, and N. Moriyama, "Shape Analysis of Pulmonary Nodules Based on Thin Section CT Images," Proc. SPIE, pp. 964-974, 1997.
[20] J. Wang, R. Engelmann, and Q. Li, "Segmentation of Pulmonary Nodules in Three-Dimensional CT Images by Use of a Spiral-Scanning Technique," Medical Physics, vol. 34, no. 12, pp. 4678-4689, 2007.
[21] S. Diciotti, G. Picozzi, M. Falchini, M. Mascalchi, N. Villari, and G. Valli, "3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Image," IEEE Trans. Information Technology in Biomedicine, vol. 12, no. 1, pp. 7-19, Jan. 2008.
[22] W. Mullally, M. Betke, J. Wang, and J.P. Ko, "Segmentation of Nodules on Chest Computed Tomography for Growth Assessment," Medical Physics, vol. 31, no. 4, pp. 839-848, 2004.
[23] W.J. Kostis, A.P. Reeves, D.F. Yankelevitz, and C.I. Henschke, "Three-Dimensional Segmentation and Growth-Rate Estimation of Small Pulmonary Nodules in Helical CT Images," IEEE Trans. Medical Imaging, vol. 22, no. 10, pp. 1259-1274, Oct. 2003.
[24] J.M. Kuhnigk, V. Dicken, L. Bornemann, A. Bakai, D. Wormanns, S. Krass, and H.O. Peitgen, "Morphological Segmentation and Partial Volume Analysis for Volumetry of Solid Pulmonary Lesions in Thoracic CT Scans," IEEE Trans. Medical Imaging, vol. 25, no. 4, pp. 417-434, Apr. 2006.
[25] T.W. Way, L.M. Hadjiiski, B. Sahiner, H.P. Chan, P.N. Cascade, E.A. Kazerooni, N. Bogot, and C. Zhou, "Computer-Aided Diagnosis of Pulmonary Nodules on CT Scans: Segmentation and Classification Using 3D Active Contour," Medical Physics, vol. 33, no. 7, pp. 2323-2337, 2006.
[26] W.E. Lorensen and H.E. Cline, "Marching Cubes: A High Resolution Three-Dimensional Surface Construction Algorithm," Computer Graphics, vol. 21, no. 4, pp. 163-169, 1987.
[27] M. Meyer, M. Desbrun, P. Schroder, and A.H. Barr, "Discrete Differential Geometry Operators for Triangulated 2-Manifolds," Visualization and Mathematics III, H.C. Hege and K. Polthier, eds., Springer-Verlag, pp. 35-57, 2003.
[28] S. Rusinkiewicz, "Estimating Curvatures and Their Derivatives on Triangle Meshes," Proc. 3D Data Processing, Visualization, and Transmission (3DPVT), pp. 486-493, 2004.
[29] J. Goldfeather and V. Interrante, "A Novel Cubic-Order Algorithm for Approximating Principal Direction Vectors," ACM Trans. Graphics, vol. 23, no. 1, pp. 45-63, 2004.
[30] X. Chen and F. Schmitt, "Intrinsic Surface Properties from Surface Triangulation," Proc. European Conf. Computer Vision, pp. 739-743, 1992.
[31] R. Cipolla and P.J. Giblin, Visual Motion of Curves and Surfaces. Cambridge Univ. Press, 2000.
[32] D.A. Field, "Laplacian Smoothing and Delaunay Triangulations," Comm. in Applied Numerical Methods, vol. 4, pp. 709-712, 1988.
[33] J. Dunchon, "Splines Minimizing Rotation-Invariant Semi-Norms in Sobolev Spaces," Constructive Theory of Functions of Several Variables, A. Dolb and B. Eckmann, eds., pp. 85-100, Springer-Verlag, 1977.
[34] J.C. Carr, R.K. Beatson, J.B. Cherrie, T.J. Mitchell, W.R. Fright, B.C. McCallum, and T.R. Evans, "Reconstruction and Representation of 3D Objects with Radial Basis Functions," Proc. ACM SIGGRAPH '01, pp. 67-76, 2001.
[35] G. Turk and J.F. O'Brien, "Shape Transformation Using Variational Implicit Functions," Proc. ACM SIGGRAPH '99, pp. 335-342, 1999.
[36] R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin, "Shape Distribution," ACM Trans. Graphics, vol. 21, no. 4, pp. 807-832, 2002.
[37] J. Pu and K. Ramani, "On Visual Similarity Based 2D Drawing Retrieval," Computer Aided Design, vol. 38, no. 3, pp. 249-259, 2006.
[38] M. Garland and P.S. Heckbert, "Surface Simplification Using Quadric Error Metrics," Proc. ACM SIGGRAPH '97, pp. 209-216, 1997.
[39] E. Magid, O. Soldea, and E. Rivlin, "A Comparison of Gaussian and Mean Curvature Estimation Methods on Triangle Meshes of Range Image Data," Computer Vision and Image Understanding, vol. 107, no. 3, pp. 139-159, 2007.
[40] H. Cantzler and R.B. Fisher, "Comparison of HK and SC Curvature Description Methods," Proc. Third Int'l Conf. 3-D Digital Imaging and Modeling (3DIM '01), pp. 285-291, 2001.
[41] J. Koenderink and A. van Doorn, "Surface Shape and Curvature Scales," Image and Vision Computing, vol. 10, no. 8, pp. 557-565, 1992.
[42] J. Pu, B. Zheng, J.K. Leader, and D. Gur, "An Automated CT Based Lung Nodule Detection Scheme Using Geometric Analysis of Signed Distance Field," Medical Physics, vol. 35, no. 8, pp. 3451-3461, 2008.
[43] J. Pu, J. Roos, C.A. Yi, S. Napel, G.D. Rubin GD, and D.S. Paik, "Adaptive Border Marching Algorithm: Automatic Lung Segmentation on Chest CT Images," Computerized Medical Imaging and Graphics, vol. 32, no. 6, pp. 452-462, 2008.
[44] M.F. McNitt-Gray et al., "The Lung Image Database Consortium (LIDC) Data Collection Process for Nodule Detection and Annotation," Academic Radiology, vol. 14, no. 12, pp. 1464-1474, 2007.

Index Terms:
Shape analysis, surface interpolation, medical image segmentation, computer-aided detection/diagnosis.
Jiantao Pu, David S. Paik, Xin Meng, Justus E. Roos, Geoffrey D. Rubin, "Shape “Break-and-Repair” Strategy and Its Application to Automated Medical Image Segmentation," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 1, pp. 115-124, Jan. 2011, doi:10.1109/TVCG.2010.56
Usage of this product signifies your acceptance of the Terms of Use.