Computerized Medical Imaging and Graphics
Volume 34, Issue 3 , Pages 236-249 , April 2010

Three-dimensional coupled-object segmentation using symmetry and tissue type information

  • Payam B. Bijari

      Affiliations

    • Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran
    • School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran, Iran
    • ,
  • Alireza Akhondi-Asl

      Affiliations

    • Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran
    • School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran, Iran
    • ,
  • Hamid Soltanian-Zadeh

      Affiliations

    • Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran
    • School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran, Iran
    • Image Analysis Laboratory, Radiology Department, Henry Ford Hospital, Detroit, MI, USA
    • Corresponding Author InformationCorresponding author at: Image Analysis Laboratory, Radiology Department, Henry Ford Hospital, One Ford Place, 2F, Detroit, MI 48202, USA. Tel.: +1 313 874 4482.

Received 13 October 2008 ,Revised 3 September 2009 ,Accepted 19 October 2009.

References 

  1. Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision. 1988;1:321–331
  2. Cohen LD. On active contour models and balloons. Graphical Model and Image Processing. 1991;53:211–218
  3. Xu C, Prince JL. Snakes, Shapes, and gradient vector flow. IEEE Transactions on Image Processing. 1998;7:359–369
  4. Xu C, Prince JL. Generalized gradient vector flow external forces for active contours. Signal Processing. 1998;71:131–139
  5. Jacob M, Blu T, Unser M. Efficient energies and algorithms for parametric snakes. IEEE Transactions on Image Processing. 2004;13:1231–1244
  6. Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1995;17:158–175
  7. Sethian JA. Level set methods and fast marching methods. Cambridge University Press; 2002;
  8. Niessen WJ, ter Haar Romeny BM, Viergever MA. Geodesic deformable models for medical image analysis. IEEE Transactions on Medical Imaging. 1998;17:634–641
  9. Sapiro G. Geometrical partial differential equations and image analysis. Cambridge University Press; 2001;
  10. Lefohn AE, Kniss JM, Hansen CD, Whitaker RT. A streaming narrow-band algorithm: interactive computation and visualization of level sets. IEEE Transactions on Visualization and Computer Graphics. 2004;10:422–433
  11. Suri JS, Liu K, Singh S, Laxminarayan SN, Zeng X, Reden L. Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the art review. IEEE Transactions on Information Technology in Biomedicine. 2002;6:8–28
  12. Bresson X. Image segmentation with variational active contours. Ph.D. Thesis. University of Lausanne, Lausanne; 2005.
  13. Woolrich MW, Behrens E. Variational Bayes inference of spatial mixture models for segmentation. IEEE Transactions on Medical Imaging. 2006;25:1380–1391
  14. Rajapakse JC, Giedd JN, Rapoport J. Statistical approach to segmentation of single channel cerebral MR images. IEEE Transactions on Medical Imaging. 1997;16:176–186
  15. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging. 2001;20:45–57
  16. Tu Z, Zheng S, Yuille AL, Reisse AL, Dutton RA, Lee AD, et al. Automated extraction of the cortical sulci based on supervised learning approach. IEEE Transactions on Medical Imaging. 2007;26:541–552
  17. Woolrich MW, Behrens TE. Variational Bayes inference of spatial mixture models for segmentation. IEEE Transactions on Medical Imaging. 2006;25:1380–1390
  18. Theodoridis S, Kourtoumbas K. Pattern recognition. Academic Press; 1998;
  19. Udupa JK, Saha PK. Fuzzy connectedness in image segmentation. Proceedings of IEEE Emerging Medical Imaging Technology. 2003;91:1649–1669
  20. Kao CY, Hofer M, Sapiro G, Stern J, Rehm K, Rottenberg DA. A geometric method for automatic extraction of Sulcal Fundi. IEEE Transactions on Medical Imaging. 2007;26:1168–1171
  21. Bijari PB, Akhoundi-Asl AR, Soltanian-Zadeh H. Thalamus segmentation in MRI using region and gradient information in level sets. In: Proceedings of the 14th Iranian conference on Elec. Eng. (ICEE’06). Tehran, Iran, May 16–18. 2006;
  22. Paragios N, Deriche R. Geodesic active regions and level set methods for supervised texture segmentation. International Journal of Computer Vision. 2002;46(3):223–247
  23. Tsai A, Yezzi A, Willsky AS. Curve evolution implementation of the Mumford Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing. 2001;10:1278–1301
  24. Chan TF. Active contours without edges. IEEE Transactions on Image Processing. 2001;10:266–277
  25. Zeydabadinezahad M. Multi-resolution segmentation of MR brain images. M.S. Thesis. University of Tehran; September 2004.
  26. Huang X, Qian Z, Huang R, Metaxas D. Deformable-model based textured object segmentation. In: International workshop on energy minimization methods in computer vision and pattern recognition. 2005;p. 119–135
  27. Delibasis K, Undrill PE. Designing texture filters with genetic algorithm: an application to medical images. Signal Processing. 1993;57:19–33
  28. Tsai A, Wells W, Tempany C, Grimson E, Willsky A. Mutual information in coupled multi-shape model for medical image segmentation. Medical Image Analysis. 2004;8:429–445
  29. Yang J, Staib LH, Duncan JS. Neighbor-constrained segmentation with level set based 3D deformable models. IEEE Transactions on Medical Imaging. 2004;23:940–948
  30. Montillo AA. Shape priors in medical image analysis: extensions of the level set method. University of Pennsylvania. Technical Report # MS-CIS-02-08; February 2002.
  31. Leventon ME, Grimson WEL, Faugeras O. Statistical shape influence in geodesic active contours. In: IEEE international conference on computer vision and pattern recognition, vol. 1. 2000;p. 1316–1323
  32. Pohl KM, Fisher J, Kikinis R, Grimson WEL, Wells WM. Shape based segmentation of anatomical structures in magnetic resonance images. In: International conference on computer vision, vol. 3765. 2005;p. 489–498
  33. Hong BW, Prados E, Soatto S, Vese L. Shape representation based on integral kernels: application to image matching and segmentation. In: IEEE computer society conference on computer vision and pattern recognition, vol. 1. 2006;p. 833–840
  34. Chan T, Zhu W. Level set based shape prior segmentation. Technical Report 03-66, Computational Applied Mathematics, UCLA, Los Angeles; 2003.
  35. Huang A, Nielson GM, Razdan A, Farin GE, Baluch DP, Capco DG. Thin structure segmentation and visualization in three dimensional biomedical images: a shape-based approach. IEEE Transactions on Visualization and Computer Graphics. 2006;12:93–102
  36. Colliot O, Camara O, Bloch I. Integration of fuzzy spatial relations in deformable models-application to brain MRI segmentation. The Journal of Pattern Recognition Society. 2006;39:1401–1414
  37. Xia Y, Bettinger K, Shen L, Reiss AL. Automatic segmentation of caudate nucleus from human brain MR images. IEEE Transactions on Medical Imaging. 2007;26:509–517
  38. Akselrod-Ballin A, Galun M, Gomori JM, Brandt A, Basri R. Prior knowledge driven multiscale segmentation of brain MRI. MICCAI. 2007;10:118–126
  39. Herbulot A, Jehan-Besson S, Barlaud M, Aubert G. Information theory for image segmentation using shape gradient. University of Nice. Technical report ISRN I3S/PR-2004-43-FR; December 2004.
  40. Aubert G, Barlaud M, Faugeras O, Jehan-Besson S. Image segmentation using active contours: calculus of variations or shape gradients?. SIAM Applied Mathematics. 2003;63(6):2128–2154
  41. Henry F, Gray RS, Pickering T. Pick Gray's anathomy. Great Britain: Chancellor Press; 1985;
  42. Osher S, Sethian J. Fronts propagating with curvature dependant algorithms based on Hamilton–Jacobi formulation. Journal of Computational Physics. 1988;79:12–49
  43. Jehan-Besson S, Barlaud M, Aubert G. DREAMS: driven by an Eulerian Accurate Minimization Method for image and video segmentation. International Journal of Computer Vision. 2003;53:45–70
  44. Liu Y, Collins RT, Rothfus WE. Automatic extraction of the central symmetry (mid-sagittal) plane from neuroradiology images. Carnegie Mellon Univ., Pittsburgh, PA: The Robotics Institute. Technical Report. CMU-RI-TR-96-40; 1996.
  45. Tuzikov AV, Colliot O, Bloch I. Brain symmetry plane computation in MR images using inertia axes and optimization. In: International conference on pattern recognition, vol. 1. 2002;p. 516–519
  46. Center for Morphometric Analysis at Massachusetts General Hospital. http://www.cma.mgh.harvard.edu/ibsr/index.html.
  47. Medical visualization and processing environment for research. http://www.Slicer.org.
  48. Lu Y, Tan CL, Huang W, Fan L. An approach to word image matching based on weighted Hausdorff distance. International Conference on Document Analysis and Recognition. 2001;6:921–925
  49. National library for medicine insight segmentation and registration toolkit. www.itk.org.
  50. Welcome trust center for neuro imaging. www.fil.ion.ucl.ac.uk/spm.
  51. Bahmanbijari P, Akhondi-Asl A, Soltanian-Zadeh H. Interactive coupled object segmentation using symmetry and distance constraints. In: Proceedings of the third Cairo international biomedical engineering conference (CIBEC’06). Cairo, Egypt, December 21–24. 2006;
  52. Bijari PB. Segmentation of brain structure from magnetic resonance images using interactive deformable models. M.S. Thesis. University of Tehran; February 2007.
  53. Udupa JK, Saha PK. Fuzzy connectedness in image segmentation. In: Proceedings of IEEE, emerging medical imaging technology, vol. 91. 2003;p. 1649–1669
  54. Dawant BM, Hartmann SL, Thirion J-P, Maes F, Vandermeulen D, Demaerel P. Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I. Methodology and validation on normal subjects. IEEE Transactions on Medical Imaging. 1999;18:909–916
  55. Worth AJ, Makris N, Patti R, Goodman JM, Hoge EA, Caviness VS, et al. Precise segmentation of the lateral ventricles and caudate nucleus in MR brain images using anatomically driven histograms. IEEE Transactions on Medical Imaging. 1998;17:303–309
  56. Batmanghelich N, Soltanian-Zadeh H, Araabi BN. Knowledge-based segmentation using simultaneous shape priori and histogram information to segment brain structures. In: IASTED SIP Conf.. Honolulu, Hawaii, August 15–17. 2005;
  57. Batmanghelich N. Atlas-based deformable models for segmentation of brain structures in MRI. MS Thesis. University of Tehran; 2005.
  58. Wu Y, Pohl K, Warfield SK, Cuttmann CRG. Automated segmentation of cerebral ventricular compartments. In: Proc. of international society for magnetic resonance in medicine, 11th scientific meeting, and exhibition, program no. 906. Ontario, Canada, July 10–16. 2003;

PII: S0895-6111(09)00130-X

doi: 10.1016/j.compmedimag.2009.10.002

Computerized Medical Imaging and Graphics
Volume 34, Issue 3 , Pages 236-249 , April 2010

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