Volumetric Mri Analysis Of Dyslexic Subjects Using A Level Set Framework

  • Manuel F. Casanova
  • H. Abd El Munim
  • Aly A. Farag
  • N. Youssry El-Zehiry
  • Rachid Fahmi
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

The minicolumn is considered as an elementary unit of the neocortex in all mammalian brains. It is believed that enlargement of the cortical surface occurs as a result of the addition of minicolumns, not a single neuron. Hence, a modern trend to analyze developmental disorders such as dyslexia and autism is to investigate how the minicolumns in the brains of dyslexic and autistic patients vary from the minicolumns in normal brains mapping this variation into a noninvasive imaging framework such as Magnetic Resonance Imaging.


White Matter Diffusion Tensor Magnetic Resonance Image Outer Compartment Magnetic Resonance Image Slice Dyslexic Subject 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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7 References

  1. 1.
    Pugh KR, Mencl WE, Jenner AR. 2000. Functional neuroimaging studies of reading and reading disability: (developmental dyslexia). Ment Retard Dev Disabil Res Rev 6(3):207-213.CrossRefGoogle Scholar
  2. 2.
    Brambilla P, Hardan A, Nemi SV. 2003. Brain anatomy and development in autism review of MRI studies. Brain Res Bull 61(6):557-569.CrossRefGoogle Scholar
  3. 3.
    Buxhoeveden DP, Casanova MF. 2002. The minicolumn and evolution of the brain: a review. Brain Behav Evol 60(3):125-51.CrossRefGoogle Scholar
  4. 4.
    Palmen S, Engeland H, Hof P, Schmitz C. 2004. Neuropathological findings in autism. Brain, 127(12):2572-2583.CrossRefGoogle Scholar
  5. 5.
    Eliez S, Rumsey JM, Giedd JN, Schmitt JE, Patwardhan AJ, Reiss AL. 2000. Morphological alteration of temporal lobe gray level matter in dyslexia: an MRI study. J Child Psychol Psychiatry 41(5):637-44.CrossRefGoogle Scholar
  6. 6.
    Goldenberg R, Kimmel R, Rivlin E, Rudzsky M. 2002. Cortex segmentation: a fast variational geometric approach. IEEE Trans Med Imaging 21(2):1544-1551.CrossRefGoogle Scholar
  7. 7.
    Baillard C, Barillot C. 2001. Robust 3D segmentation of anatomical structures with level sets. Med Image Anal 5(3):185-94.CrossRefGoogle Scholar
  8. 8.
    Zeng X, Staib LH, Schultz RT, Tagare H, Win L, Duncan JS. 1999. A new approach to 3D sulcal ribbon finding from MR images. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’99). Lecture notes in computer science, Vol. 1679, pp. 148-157. New York: Springer.Google Scholar
  9. 9.
    Farag AA, Hassan H. 2004. Adaptive segmentation of multimodal 3d data using robust level set techniques. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’2004). Lecture notes in computer science, Vol. 3216, pp. 143-150, New York: Springer.Google Scholar
  10. 10.
    Rousson M, Paragios N, and Deriche R. 2004. Implicit active shape models for 3d seg- mentation in mri imaging. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’2004). Lecture notes in computer science, Vol. 3216, pp. 209-216. New York: Springer.Google Scholar
  11. 11.
    Zeng X, Staib LH, Duncan JS. Volumetric layer segmentation using coupled surface propagation. In Proceedings of the IEEE international conference on computer vision and pattern recognition, pp. 708-715. Washington, DC: IEEE Computer Society.Google Scholar
  12. 12.
    Caselles V, Kimmel R, Sapiro G. 1997. Geodesic active contours. Int J Comput Vision 22(1):61-79.MATHCrossRefGoogle Scholar
  13. 13.
    Zaho H-K, Chan T, Merriman B, Osher S. 1995. A variational level set approach to multiphase motion. J Comput Phys 127:179-195.CrossRefGoogle Scholar
  14. 14.
    Xu C, Pham D, Prince J. 1999. Reconstruction of the human cerebral cortex from magnetic resonance images. IEEE Trans Med Imaging 18(6):467-480.CrossRefGoogle Scholar
  15. 15.
    Casanova M, Switala A, Trippe J, Fobbs A. 2004. Minicolumnar Morphometry: a report on the brains of Yakovlev, Meyer, And geschwind. Technical Report, Department of Psychiatry and Behavior Science, University of Louisville, Louisville, Kentucky.Google Scholar
  16. 16.
    Makris N, Meyer JW, Bates JF, Yeterian EH, Kennedy DN, Caviness VS. 1999. MRI-based topographic parcellation of human cerebral white matter. Neuroimage 9(1):18-45.CrossRefGoogle Scholar
  17. 17.
    Mountcastle VB. 1997. The minicolumnar organization of the neocortex. Brain 120:701-722.CrossRefGoogle Scholar
  18. 18.
    Herbert MR, Zeigler DA, Markis N. 2004. Localization of white matter volume increase in autism and developmental language disorders. Ann Neurol 55:530-540.CrossRefGoogle Scholar
  19. 19.
    Chung M, Robbins S, Dalton K. 2005. Cortical thickness analysis in autism with heat kernel smoothing. NeuroImage 25:1256-1265.CrossRefGoogle Scholar
  20. 20.
    Jackowski M, Kao C, Qiu M, ConsTable R, Staib L. 2005. White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging. Med Image Anal 9:427-440.CrossRefGoogle Scholar
  21. 21.
    Lenglet C, Deriche R, Faugeras O. 2003. Diffusion tensor magnetic resonance imaging: brain connectivity mapping. Research Report no. 4983, Institut National de Rechereche en informatique et automatique, Sophia-Antipolis, France.Google Scholar
  22. 22.
    Bihan D, Breton E. 1985. Imagerie de diffusion in vivi par resonance magnitique nucleaire. CR Acad Sci Paris 301:1109-1112.Google Scholar
  23. 23.
    Merboldt K, Hanicke W, Frahm J. 1985. Self-diffusion NMR imaging using stimulated echoes. J Magn Reson 64:479-486.Google Scholar
  24. 24.
    Bihan D, Mangin J, Poupon C. 2001. Diffusion tensor imaging: concepts and applications. J Magn Reason Imag 13:534-546.CrossRefGoogle Scholar
  25. 25.
    Goraly N, Kwon H, Eliez S. 2004. White matter structures in autism: preliminary evidence from diffusion tensor imaging. Biol Psychiatry 55 :323-326.CrossRefGoogle Scholar
  26. 26.
    Eckert MA, Leonard CM, Wilke M, Eckert M, Richards T, Richards A, Berninger V. 2004. Anatomical signatures of dyslexia in children: unique information from manual and voxel based morphometry brain measures. Cortex 41(3):304-315.CrossRefGoogle Scholar
  27. 27.
    El-Zehiry N, Casanova M, Hassan H, Farag A. 2005. Structural MRI analysis of the brains of patients with dyslexia. In Proceedings of the international conference on computer assisted radiology and surgery (CARS 2005), pp. 1291-1296. New York: Elsevier.Google Scholar
  28. 28.
    Rumsey JM, Donohue BC, Brady DR, Nace K, Giedd JN, Andreason P. 1997. A magnetic resonance study of planum temporale asymmetry in men with developmental dyslexia. Arch Neuorol 54(12):1481-1489.Google Scholar
  29. 29.
    Rumsey JM, Nace K, Donohue BC, Wise D, Maisog JM, Andreason P. A positron emission tomographic study of impaired word recognition and phonological processing in dyslexic men. Arch Neurol 54(5):562-573.Google Scholar
  30. 30.
    Chan T, Vese L. 2002. A multiphase level set framework for image segmentation using the mumford and shah model. Int J Comput Vision 50(3):271-293.MATHCrossRefGoogle Scholar
  31. 31.
    Osher S, Paragios N. 2003. Geometric level set methods in imaging, vision, and graphics. New York: Springer.Google Scholar
  32. 32.
    Sapiro G. 2001. Geometric partial differential equations and image analysis. Cambridge: Cambridge UP.MATHGoogle Scholar
  33. 33.
    Caselles V, Kimmel R, Sapiro G. 1997. Geodesics active contours. Int J Comput Vision 22:61-97.MATHCrossRefGoogle Scholar
  34. 34.
    Epstein CL, Gage M. 1987. The curve shhortening flow. In Wave motion: theory modeling and computation, pp. 15-59. Ed A Chorin, A Majda. New York: Springer.Google Scholar
  35. 35.
    Paragios N, Deriche R. 1999. Unifying boundary and region-based information for geodesic active tracking. In Proceedings of the IEEE international conference on computer vision and pattern recognition (CVPR’99), Vol. 2, pp. 300-305. Washington, DC: IEEE Computer Society.Google Scholar
  36. 36.
    Gomes J, Faugeras O. Reconciling distance functions and Level Sets. Technical Report no 3666, INRIA, Institut National de Rechereche en informatique et automatique, Sophia- Antipolis, France.Google Scholar
  37. 37.
    Sethian JA. 1999. Level set methods and fast marching methods. Cambridge: Cambridge UP.MATHGoogle Scholar
  38. 38.
    Malladi R, Sethian J, Vemuri B. 1995. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Machine Intell 17(2):158-175.CrossRefGoogle Scholar
  39. 39.
    Samson C, Blanc-Féraud L, Aubert G, Zerubia J. 1999. Multiphase evolution and variational image classification. Technical Report no. 3662, INRIA, Institut National de Rechereche en informatique et automatique, Sophia-Antipolis, France.Google Scholar
  40. 40.
    Osher S. UCLA Technical Report, available at
  41. 41.
    Sethian JA. 1996. Level set methods: evolving interfaces in geometry, fluid mechanics, computer vision and material sciences. Cambridge: Cambridge UP.Google Scholar
  42. 42.
    Osher SJ, Sethian JA. 1988. Front propagation with curvature speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79:12-49.MATHCrossRefMathSciNetGoogle Scholar
  43. 43.
    Chan T, Sandberg B, Vese L. 2000. Active contours without edges for vector valued images. J Vis Commun Image Represent 2:130-141.CrossRefGoogle Scholar
  44. 44.
    Duda R, Hart P, Stork D. 2001. Pattern classification. New York: John Wiley and Sons.MATHGoogle Scholar
  45. 45.
    Osher S, Fedkiw R. 2003. Level set methods and dynamic implicit surfaces. New York: Springer.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Manuel F. Casanova
    • 1
  • H. Abd El Munim
    • 2
  • Aly A. Farag
    • 3
  • N. Youssry El-Zehiry
    • 2
  • Rachid Fahmi
    • 2
  1. 1.Department of Psychiatry and Behavioral SciencesUniversity of LouisvilleLouisvilleUSA
  2. 2.Computer Vision and Image Processing Laboratory, Department of Electrical and Computer EngineeringUniversity of LouisvilleLouisvilleUSA
  3. 3.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisvilleUSA

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