Skip to main content

Morphometric Analysis Of Normal And Pathologic Brain Structure Via High-Dimensional Shape Transformations

  • Chapter
Deformable Models

The widespread use of neuroimaging methods in a variety of clinical and basic science fields has created the need for systematic and highly automated image analysis methodologies that extract pertinent information from images, in a way that enables comparisons across different studies, laboratories, and image databases. Quantifying the morphological characteristics of the brain from tomographic images, most often from magnetic resonance images (MRIs), is important for understanding the way in which a disease can affect brain anatomy, for constructing newdiagnostic methods utilizing image information, and for longitudinal follow-up studies evaluating potential drugs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

7 References

  1. Davatzikos C, Resnick SM. 2002. Degenerative age changes in white matter connectivity visualized in vivo using magnetic resonance imaging. Cereb Cortex 12:767-771.

    Article  Google Scholar 

  2. Davatzikos C, Genc A, Xu D, Resnick SM. 2001. Voxel-based morphometry using the Ravens maps: methods and validation using simulated longitudinal atrophy. NeuroImage 14:1361-1369.

    Article  Google Scholar 

  3. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. 2003. Longitudinal mag- netic resonance imaging studies of older adults: a shrinking brain. J Neurosci 23:3295-3301.

    Google Scholar 

  4. Shen D, Liu D, Liu H, Clasen L, Giedd J, Davatzikos C. 2004. Automated morphometric study of brain variation in xxy males. NeuroImage 23:648-653.

    Article  Google Scholar 

  5. Bobinski M, de Leon MJ, Convit A, De Santi S, Wegiel J, Tarshish CY, Saint Louis LA, Wisniewski HM. 1999. MRI of entorhinal cortex in mild Alzheimer’s disease. Lancet 353:38-40.

    Article  Google Scholar 

  6. Convit A, De Leon MJ, Tarshish C, De Santi S, Tsui W, Rusinek H, George A, 1997. Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiol Aging 18:131-138.

    Article  Google Scholar 

  7. Cuenod CA, Denys A, Michot JL, Jehenson P, Forette F, Kaplan D, Syrota A, Boller F. 1993. Amygdala atrophy in Alzheimer’s disease: an in vivo magnetic resonance imaging study. Arch Neurol 50:941-945.

    Google Scholar 

  8. De Santi S, de Leon MJ, Rusinek H, Convit A, Tarshish CY, Roche A, Tsui WH, Kandil E, Boppana M, Daisley K, Wang GJ, Schlyer D, Fowler J. 2001. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging 22:529-539.

    Article  Google Scholar 

  9. deToledo-Morrell L, Sullivan MP, Morrell F, Wilson RS, Bennett DA, Spencer S. 1997. Alzheimer’s disease: in vivo detection of differential vulnerability of brain regions. Neurobiol Aging 18:463-438.

    Article  Google Scholar 

  10. Dickerson BC, Goncharova I, Sullivan MP, Forchetti C, Wilson RS, Bennett DA, Beckett LA, deToledo-Morrell L. 2001. MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiol Aging 22:747-754.

    Article  Google Scholar 

  11. Du AT, Schuff N, Amend D, Laakso MP, Hsu YY, Jagust WJ, Yaffe K, Kramer JH, Reed B, Norman D, Chui HC, Weiner MW. 2001. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry 71:441-447.

    Article  Google Scholar 

  12. Frisoni GB, Beltramello A, Weiss C, Geroldi C, Bianchetti A, Trabucchi M. 1996. Linear measures of atrophy in mild Alzheimer disease. Am J Neuroradiol 17:913-923.

    Google Scholar 

  13. Jack Jr CR, Petersen RC, Xu YC, Waring SC, O’Brien PC, Tangalos EG, Smith GE, Ivnik RJ, Kokmen E. 1997. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology 49:786-794.

    Google Scholar 

  14. Jack CR, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Waring SC, Tangalos E, Kokmen E. 1999. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52:1397-1403.

    Google Scholar 

  15. Killiany RJ, Moss MB, Albert MS, Sandor T, Tieman J, Jolesz F. 1993. Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer’s disease. Arch Neurol 50:949-954.

    Google Scholar 

  16. Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, Tanzi R, Jones K, Hyman. BT, Albert MS. 2000. Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Ann Neurol 47:430-439.

    Article  Google Scholar 

  17. Krasuski JS, Alexander GE, Horwitz B, Daly EM, Murphy DG, Rapoport SI, Schapiro MB. 1998. Volumes of medial temporal lobe structures in patients with Alzheimer’s disease and mild cognitive impairment (and in healthy controls). Biol Psychiatry 43:60-68.

    Article  Google Scholar 

  18. Laakso MP, Soininen H, Partanen K, Helkala EL, Hartikainen P, Vainio P, Hallikainen M, Hanninen T, Riekkinen Sr PJ. 1995. Volumes of hippocampus, amygdala and frontal lobes in the MRI-based diagnosis of early Alzheimer’s disease: correlation with memory functions. 9:73-86.

    Google Scholar 

  19. Laakso MP, Hallikainen M, Hanninen T, Partanen K, Soininen H. 2000. Diagnosis of Alzheimer’s disease: MRI of the hippocampus vs delayed recall. Neuropsychologia 38:579-584.

    Article  Google Scholar 

  20. Lehericy S, Baulac M, Chiras J, Pierot L, Martin N, Pillon B, Deweer B, Dubois B, Marsault C. 1994. Amygdalohippocampal MR volume measurements in the early stages of Alzheimer disease. Am J Neuroradiol 15:929-937.

    Google Scholar 

  21. Rosen AC, Prull MW, Gabrieli JD, Stoub T, O’Hara R, Friedman L, Yesavage JA, deToledo- Morrell L. 2003. Differential associations between entorhinal and hippocampal volumes and memory performance in older adults. Behav Neurosci 117:1150-1160.

    Article  Google Scholar 

  22. Xu Y, Jack Jr CR, O’Brien PC, Kokmen E, Smith GE, Ivnik RJ, Boeve BF, Tangalos RG, Petersen RC. 2000. Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology 54:1760-1767.

    Google Scholar 

  23. Bookstein FL. 1989. Principal warps: thin-plate splines and the decomposition of deforma- tions. IEEE Trans Pattern Anal Machine Intell 11:567-585.

    Article  MATH  Google Scholar 

  24. Miller MI, Christensen G, Amit Y, Grenander U. 1943. Mathematical textbook of deformable neuroanatomies. Proc Natl Acad Sci USA 90:11944-11948.

    Article  Google Scholar 

  25. Davatzikos C, Vaillant M, Resnick S, Prince JL, Letovsky S, Bryan RN. 1996. A computerized approach for morphological analysis of the corpus callosum. J Comput Assist Tomogr 20:88-97.

    Article  Google Scholar 

  26. Sandor S, Leahy R. 1997. Surface-based labelling of cortical anatomy using a deformable atlas. IEEE Trans Med Imaging 16:41-54.

    Article  Google Scholar 

  27. Thompson PM, MacDonald D, Mega MS, Holmes CJ, Evans A, Toga AW. 1997. Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. 21:567-581.

    Google Scholar 

  28. Ashburner J, Hutton C, Frackowiak RSJ, Johnsrude I, Price C, Friston KJ. 1998. Identifying global anatomical differences: deformation-based morphometry. Hum Brain Mapp 6:348-357.

    Article  Google Scholar 

  29. Golland P, Grimson WEL, Kikinis R. 1999. Statistical shape analysis using fixed topology skeletons: corpus callosum study. In Lecture notes in computer science, Vol. 1613, pp. 382-387. New York: Springer.

    Google Scholar 

  30. Pizer S, Fritsch DS, Yushkevich PA, Johnson VE, Chaney EL. 1999. Segmentation, registration and measurement of shape variation via image object shape. IEEE Trans Med Imaging 18:851-865.

    Article  Google Scholar 

  31. Golland P, Grimson WEL, Shenton ME, Kikinis R. 2001. Deformation analysis for shape based classification. In Lecture notes in computer science, Vol. 2082, pp. 517-530. New York: Springer.

    Google Scholar 

  32. Rexilius J, Warfield SK, Guttman CRG, Wei X, Benson R, Wolfson L, Shenton ME, Handels H, Kikinis R. 1999. A novel nonrigid registration algorithm and applications. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’98), pp. 202-209.

    Google Scholar 

  33. Christensen G, Rabbitt RD, Miller RI. 1994. 3D brain mapping using a deformable neuroanatomy. Phys Med Biol 39:609-618.

    Article  Google Scholar 

  34. Joshi S, Pizer S, Fletcher PT, Thall A, Tracton G. 2001. Multi-scale 3-D deformable model segmentation based on medial description. In Lecture notes in computer science, Vol. 2082, pp. 64-77. New York: Springer.

    Google Scholar 

  35. Szekely G, Kelemen A, Brechbuhler C, Gerig G. 1996. Segmentation of 2-D and 3-D objects from MRI volume data using constrained deformations of flexible Fourier contour and surface models. Med Image Anal 1:19-34.

    Google Scholar 

  36. Styner M, Gerig G. 2001. Medial models incorporating object variability for 3d shape analysis. In Lecture notes in computer science, Vol. 2082, pp. 502-516. New York: Springer.

    Google Scholar 

  37. Freeborough PA, Fox NC. 1998. Modeling brain deformations in Alzheimer’s disease by fluid registration of serial 3D MR images. J Comput Assist Tomogr 22:838-843.

    Article  Google Scholar 

  38. Collins L, Peters TM, Dai W, Evans AC. 1992. Model-based segmentation of individual brain structures from MRI data. Proc SPIE 1808:10-23.

    Article  Google Scholar 

  39. Thompson DW. 1917. On growth and form: Cambridge: Cambridge UP.

    Google Scholar 

  40. Grenander U. 1983. Tutorial in pattern theory: a technical report. Providence: Brown University.

    Google Scholar 

  41. Davatzikos C, Resnick SM. 1998. Sex differences in anatomic measures of interhemispheric connectivity: correlations with cognition in men but not in women. Cereb Cortex 8:635-640.

    Article  Google Scholar 

  42. Lorenzen P, Davis B, Joshi S. 2005. Unbiased atlas formation via large deformations metric mapping. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’2005). Lecture notes in computer science, Vol. 2717, pp. 411-418. New York: Springer.

    Google Scholar 

  43. Park H, Peyton HB, Hero III AO, Meyer CR. 2005. Least biased target selection in probabilistic atlas construction. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’2005). Lecture notes in computer science, Vol. 3750, pp. 419-426. New York: Springer.

    Google Scholar 

  44. Christensen GE, Johnson HJ. 2001. Consistent image registration. IEEE Trans Med Imaging 20:568-582.

    Article  Google Scholar 

  45. Collins DL, Neelin P, Peters TM, Evans AC. 1994. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192-205.

    Article  Google Scholar 

  46. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ. 1999. Non-rigid reg- istration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18:712-721.

    Article  Google Scholar 

  47. Thirion JP. 1996. Non-rigid matching using deamons. In Proceedings of the IEEE international conference on computer vision and pattern recognition (CVPR), pp. 245-151. Washington, DC: IEEE Computer Society.

    Chapter  Google Scholar 

  48. Ferrant M, Warfield S, Guttman CRG, Mulkern RV, Jolesz F, Kikinis R. 1999. 3D image match- ing using a finite element based elastic deformation model. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’98). Lecture notes in computer science, Vol. 1679. pp. 202-209. New York: Springer.

    Google Scholar 

  49. Friston KJ, Ashburner J, Frith CD, Poline JB, Heather JD, Frackowiak RSJ. 1995. Spatial registration and normalization of images. Hum Brain Mapp 2:165-189.

    Article  Google Scholar 

  50. Chung MK, Worsley KJ, Paus T, Cherif C, Collins DL, Giedd JN, Rapoport JL, Evanst AC. 2001. A unified statistical approach to deformation-based morphometry. Neuroimage 14:595-606.

    Article  Google Scholar 

  51. Christensen GE. 1999. Consistent linear-elastic transformations for image matching. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI’98). Lecture notes in computer science, vol. 1613, pp. 224-237. New York: Springer.

    Google Scholar 

  52. Shen D, Davatzikos C. 2002. HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imaging 21:1421-1439.

    Article  Google Scholar 

  53. Viola P, Wells III WM. 1997. Alignment by maximization of mutual information. Int J Comput Vision 24(2):133-154.

    Article  Google Scholar 

  54. Wells WM III, Viola P, Kikinis R. 1995. Multi-modal volume registration by maximization of mutual information. In Proceedings of the second international symposium on medical robotics and computer assisted surgery, pp. 55-62. New York: Wiley.

    Google Scholar 

  55. Davatzikos C. 1996. Spatial normalization of 3D images using deformable models. J Comput Assist Tomogr 20:656-665.

    Article  Google Scholar 

  56. Thompson P, Toga AW. 1996. A surface-based technique for warping three-dimensional im- ages of the brain. IEEE Trans Med Imaging 15:402-417.

    Article  Google Scholar 

  57. Wang Y, Staib LH. 1999. Elastic model-based non-rigid registration incorporating statistical shape information. In Proceedings of the international conference on medical image com- puting and computer-assisted intervention (MICCAI’98). Lecture notes in computer science, Vol. 1496, pp. 1162-1173. New York: Springer.

    Google Scholar 

  58. Wang Y, Peterson BS, Staib LH. 2003. 3D brain surface matching based on geodesics and local geometry. Comput Vision Image Understand 89:252-271.

    Article  Google Scholar 

  59. Rangarajan A, Chui H, Bookstein FL. 1997. The softassign procrustes matching algorithm. In Proceedings of the 15th international conference on information processing in medical imaging. Lecture notes in computer science, Vol. 1230, pp. 29-42. New York: Springer.

    Google Scholar 

  60. Chui H, Rangarajan A. 2003. A new point matching algorithm for non-rigid registration. Comput Vision Image Understand 89:114-141.

    Article  MATH  Google Scholar 

  61. Shen DG, Davatzikos C. 2003. Very high resolution morphometry using mass-preserving deformations and HAMMER elastic registration. NeuroImage 18:28-41.

    Article  Google Scholar 

  62. Shen D. 2004. 4D image warping for measurement of longitudinal brain changes. In Proceedings of the IEEE international symposium on biomedical imaging, Vol. 1, pp. 904-907. Washington, DC: IEEE Computer Society.

    Google Scholar 

  63. Xue Z, Shen D, Davatzikos C. 2003. Correspondence detection using wavelet-based attribute vectors. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI 2003). Lecture notes in computer science, Vol. 2870, pp. 762-770. New York: Springer.

    Google Scholar 

  64. Xue Z, Shen D, Davatzikos C. 2006. Determining correspondence in 3D MR brain images using attribute vectors as morphological signatures of voxels. IEEE Trans Med Imaging 25(5):626-630.

    Article  Google Scholar 

  65. Johnson HJ, Christensen G. 2001. Landmark and intensity-based consistent thin-plate spline image registration. In Proceedings of the international conference on information processing in medical imaging. Lecture notes in computer science, Vol. 2081, pp. 329-343. New York: Springer.

    Google Scholar 

  66. Davatzikos C. 2001. Measuring biological shape using geometry-based shape transformations. Image Vision Comput 19:63-74.

    Article  Google Scholar 

  67. Ashburner J, Friston KJ. 2000. Voxel-based morphometry: the methods. Neuroimage 11:805-821.

    Article  Google Scholar 

  68. Good CD, Scahill RI, Fox NC, Ashburner J, Friston KJ, Chan D, Crum WR, N. Rossor M Frackowiak RSJ. 2002. Automatic differentiation of anatomical patterns in the human brain: Validation with studies of degenerative dementias. Neuroimage 17:29-46.

    Article  Google Scholar 

  69. Goldszal AF, Davatzikos C, Pham D, Yan M, Bryan RN, Resnick SM. 1998. An image processing protocol for the analysis of MR images from an elderly population. J Comput Assist Tomogr 22:827-837.

    Article  Google Scholar 

  70. Davatzikos C. 1998. Mapping of image data to stereotaxic spaces. Hum Brain Mapp 6:334-338.

    Article  Google Scholar 

  71. Shen D, Davatzikos C. 2004. Measuring temporal morphological changes robustly in Brain MR images via 4-dimensional template warping. NeuroImage 21:1508-1517.

    Article  Google Scholar 

  72. Lao Z, Shen D, Xue Z, Karacali B, Resnick SM, Davatzikos C. 2003. Morphological classifi- cation of brains via high-dimensional shape transformations and machine learning methods. Neuroimage 21:46-57.

    Article  Google Scholar 

  73. Gerig G, Styner M, Lieberman J. 2001. Shape versus size: improved understanding of the mor- phology of brain structures. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI 2001). Lecture notes in computer science, Vol. 2208, pp. 24-32. New York: Springer.

    Google Scholar 

  74. Braak H, Braak E, Bohl J, Bratzke H. 1998. Evolution of Alzheimer’s disease related cortical lesions. J Neural Transm (suppl) 54:97-106.

    Google Scholar 

  75. Moffat SD, Szekely CA, Zonderman AB, Kabani NJ, Resnick SM. 2000. Longitudinal change in hippocampal volume as a function of apolipoprotein E genotype. Neurology 55:134-136.

    Google Scholar 

  76. Resnick SM, Goldszal A, Davatzikos C, Golski S, Kraut MA, Metter EJ, Bryan RN, Zonder- man AB. 2000. One-year age changes in MRI brain volumes in older adults. Cereb Cortex 10:464-472.

    Article  Google Scholar 

  77. Dawant BM, Hartmann SL, Gadamsetty S. 1999. Brain atlas deformation in the presence of large space-occupying tumours. In Proceedings of the international conference on medical im- age computing and computer-assisted intervention (MICCAI’99). Lecture notes in computer science, Vol. 1679, pp. 589-596. New York: Springer.

    Google Scholar 

  78. Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Villemure J-G, Thiran J-P. 2004. Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans Med Imaging 23:1301-1314.

    Article  Google Scholar 

  79. Kyriacou S, Davatzikos C, Zinreich S, Bryan R. 1999. Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model. IEEE Trans Med Imaging 18:580-592.

    Article  Google Scholar 

  80. Kansal AR, Torquato S, Harsh IGR, Chiocca EA, Deisboeck TS. 2000. Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J Theor Biol 203:367-382.

    Article  Google Scholar 

  81. Wasserman R, Acharya R. 1996. A patient-specific in vivo tumor model [review]. Math Biosci 136:111-140.

    Article  MATH  Google Scholar 

  82. Greenspan HP. 1976. On the growth and stability of cell cultures and solid tumors. J Theor biol 56:229-242.

    Article  MathSciNet  Google Scholar 

  83. Kuroiwa T, Ueki M, Suemasu H, Taniguchi I, Okeda R. 1997. Biomechanical characteris- tics of brain edema: the difference between vasogenic-type and cytotoxic-type edema. Acta Neurochir Suppl (Wien) 60:158-161.

    Google Scholar 

  84. Nagashima T, Tamaki N, Takada M, Tada Y. 1994. Formation and resolution of brain edema associated with brain tumors: a comprehensive theoretical model and clinical analysis. Acta Neurochir Suppl (Wien) 60:165-167.

    Google Scholar 

  85. Skrinjar O, Nabavi A, Duncan J. 2002. Model-driven brain shift compensation. Med Image Anal 6:361-373.

    Article  Google Scholar 

  86. Ferrant M, Warfield S, Nabavi A, Macq B, Kikinis R. 2000. Registration of 3D intraoperative MR images of the brain using a finite element biomechanical model. In Proceedings of the international conference on medical image computing and computer-assisted intervention (MICCAI 2000). Lecture notes in computer science, Vol. 1935, pp. 19-28. New York: Springer

    Google Scholar 

  87. Hagemann A, Rohr K, Stiehl HS. 2002. Coupling of fluid and elastic models for biomechanical simulations of brain deformations using FEM. Med Image Anal 6:375-388.

    Article  Google Scholar 

  88. Clatz O, Sermesant M, Bondiau P-Y, Delingette H, Warfield SK, Malandain G, Ayache N. 2005. Realistic simulation of the 3D growth of brain tumors in MR images coupling diffusion with mass effect. IEEE Trans Med Imaging 24:1334-1346.

    Article  Google Scholar 

  89. Takizawa H, Sugiura K, Baba M, Miller JD. 1994. Analysis of intracerebral hematoma shapes by numerical computer simulation using the finite element method. Neurol Med-Chir 34:65-69.

    Article  Google Scholar 

  90. Mendis KK, Stalnaker R, Advani SH. 1995. A constitutive relationship for large-deformation finite-element modeling of brain tissue. J Biomech Eng 117:279-285.

    Article  Google Scholar 

  91. Miller K, Chinzei K. 1997. Constitutive modelling of brain tissue: experiment and theory. J Biomec30:1115-1121.

    Article  Google Scholar 

  92. Miller K, Chinzei K. 2002. Mechanical properties of brain tissue in tension. J Biomech 35:483-490.

    Article  Google Scholar 

  93. Prange MT, Margulies SS. 2002. Regional, directional, and age-dependent properties of the brain undergoing large deformation. J Biomech Eng 124:244-252.

    Article  Google Scholar 

  94. Miga M, Paulsen K, Kennedy FE, Hartov A, Roberts D. 1999. Model-updated image-guided neurosurgery using the finite element method: incorporation of the falx cerebri. IEEE Trans Med Imaging 18(10):866-874.

    Article  Google Scholar 

  95. Miga M, Paulsen K, Kennedy FE, Hoopes J, Hartov A, Roberts D. 1998. Initial in-vivo analysis of 3d heterogeneous brain computations for model-updated image-guided neurosurgery. In Proceedings of the international conference on medical image computing and computerassisted intervention (MICCAI’98). Lecture notes in computer science, Vol. 1496, pp. 743- 752. New York: Springer.

    Google Scholar 

  96. ABAQUS, Version 6.1. 2000. Warwick, RI: Hibbitt, Karlsson, and Sorensen Inc.

    Google Scholar 

  97. Mohamed A. 2005. Combining statistical and biomechanical models for estimation of anatom- ical deformations. PhD dissertation. Baltimore: Johns Hopkins University.

    Google Scholar 

  98. Mohamed A, Kyriacou SK, Davatzikos C. A statistical approach for estimating brain tumor induced deformation. In Proceedings of the IEEE workshop on mathematical methods in bio- medical image analysis (MMBIA’01), pp. 52-57. Washington, DC: IEEE Computer Society.

    Google Scholar 

  99. Mohamed A, Davatzikos C. 2004. Finite element mesh generation and remeshing from seg- mented medical images. In Proceedings of the IEEE workshop on mathematical methods in biomedical image analysis (MMBIA’04), Vol. 1, pp. 420-423.

    Google Scholar 

  100. Rivara MC. 1997. New longest-edge algorithms for the refinement and/or improvement of unstructured triangulations. Int J Num Methods Eng 40:3313-3324.

    Article  MATH  MathSciNet  Google Scholar 

  101. Wodinsky I, Kensler C, Roll D. 1969. The induction and transplantation of brain tumors in neonatal beagles. Proc Am Assoc Cancer Res 10:99.

    Google Scholar 

  102. Studholme C, Hill DLG, Hawkes DJ. 1999. An overlap invariant entropy measure of 3d medical image alignment. Pattern Recognit 32:71-86.

    Article  Google Scholar 

  103. Mohamed A, Davatzikos C. 2004. Shape representation via best orthogonal basis selection. In Proceedings of the international conference on medical image computing and computer- assisted intervention (MICCAI 2000). Lecture notes in computer science, Vol. 3216, pp. 225-233.

    Google Scholar 

  104. Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM. 2003. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet (Neurol) 2:79-88.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Mohamed, A., Davatzikos, C. (2007). Morphometric Analysis Of Normal And Pathologic Brain Structure Via High-Dimensional Shape Transformations. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68343-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-68343-0_12

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-31204-0

  • Online ISBN: 978-0-387-68343-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics