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Robust Neuroimaging-Based Classification Techniques Of Autistic Vs. Typically Developing Brain

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Deformable Models

Autism is a developmental disorder characterized by social deficits, impaired communication, and restricted and repetitive patterns of behavior (American Psychiatry Association, 2000).

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6 References

  1. Minshew NJ, JB Payton. 1988. New perspectives in autism, part I: the clinical spectrum of autism. J Curr Probl Pediatr 18:561-610.

    Google Scholar 

  2. Yeargin-Allsop M, Rice C, Karapurkar T, Doernber N, Boyle C, Murphy C. 2003. Prevalence of autism in a US metropolitan area. JAMA 289:48-55.

    Article  Google Scholar 

  3. Tidmarsh L, Volkmar FR. 2003. Diagnosis and epidemiology of autism spectrum disorders. Can J Psychiatry 48(8):517-525.

    Google Scholar 

  4. Wallis C. 2006. Inside the autistic mind. Time 167(20):43-51.

    Google Scholar 

  5. Bolton P, Macdonald H, Pickles A, Rios P, Goode S, Crowson M, Bailey A, Rutter M. 1994. A case-control family history study of autism. J Child Psychol Psychiatry 35:877-900.

    Article  Google Scholar 

  6. Minshew NJ, Payton JB. 2003. New perspectives in autism, part II: the differential diagnosis and neurobiology of autism. Curr Probl Pediatr 18:613-694.

    Google Scholar 

  7. Stevens MC, Fein DA, Dunn M, Allen D, Waterhouse LH, Feinstein C, Rapin I. 2000. Sub- groups of children with autism by cluster analysis: a longitudinal examination. J Am Acad Child Adolesc Psychiatry 39:346-352.

    Article  Google Scholar 

  8. Kanner L. 1943. Autistic disturbances of affective contact. J Nervous Child 2:250-250.

    Google Scholar 

  9. Bailey A, Luthert P, Bolton P, Le Couteur A, Rutter M, Hardin B. 1993. Autism and megalencephaly. Lancet 341:1225-1226.

    Article  Google Scholar 

  10. Davidovitch M, Patterson B, Gartside P. 1996. Head circumference measurements in children with autism. J Child Neurol 11:389-393.

    Article  Google Scholar 

  11. Miles JH, Hadden LL, Takahashi TN, Hillman RE. 2000. Head circumference is an indepen- dent clinical finding associated with autism. Am J Med Gen 95:339-350.

    Article  Google Scholar 

  12. Aylward EH, Minshew NJ, Field K, Sparks BF, Singh N. 2002. Effects of age on brain volume and head circumference in autism. Neurology 59(2):175-183.

    Google Scholar 

  13. Courchesne R, Carper R, Akshoomoff N. 2003. Evidence of brain overgrowth in the first year of life in autism. JAMA 290:337-344.

    Article  Google Scholar 

  14. Steg JP, Rapoport JL. 1975. Minor physical anomalies in normal, neurotic, learning disabled, and severely disturbed children. J Autism Dev Disord, 5(4):299-307.

    Article  Google Scholar 

  15. Walker HA. 1997. Incidence of minor physical anomaly in autism. J Autism Child Schizophr 7(2):165-176.

    Article  Google Scholar 

  16. Woodhouse W, Bailey A, Rutter M, Bolton P, Baird G, Le Couteur A. 1996. Head circumference in autism and other pervasive developmental disorders. J Child Psychol Psychiatry 37(6):665-671.

    Article  Google Scholar 

  17. Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, Tigue ZD, Chisum HJ, Moses P, Pierce K, Lord C, Lincoln AJ, Pizzo S, Schreibman L, Haas RH, Akshoomoff NA., Courchesne RY. 2001. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57(2):245-254.

    Google Scholar 

  18. Sparks B, Friedman S, Shaw D, Aylward E, Echelard D, Artru A, Maravilla K, Giedd J, Munson J, Dawson G, Dager S. 2002. Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59(2):184-192.

    Google Scholar 

  19. Kemper TL, Bauman ML. 1993. The contribution of neuropathologic studies to the understanding of autism. J Neurol Clin 11(1):175-187.

    Google Scholar 

  20. Guerin P, Lyon G, Barthelemy C, Sostak E, Chevrollier V, Garreau B, Lelord G. 1996. Neuropathological study of a case of autistic syndrome with severe mental retardation. Neurology 38(3):203-211.

    Google Scholar 

  21. Egaas B, Courchesne E, Saitoh O. 1995. Reduced size of corpus callosum in autism. J Arch Neurol 52(8): 794-801.

    Google Scholar 

  22. Hardan AY, Minshew NJ, Keshavan MS. 2000. Corpus callosum size in autism. Neurology 55:1033-1036.

    Google Scholar 

  23. Piven J, Bailey J, Ranson BJ, Arndt S. 1997. An MRI study of the corpus callosum in autism. Am J Psychiatry 154(8):1051-1056.

    Google Scholar 

  24. Haas R, Townsend J, Courchesne E, Lincoln A, Schreibman L, Yeung-Courchesne R. 1996. Neurologic abnormalities in infantile autism. J Child Neurol 11(2):84-92.

    Article  Google Scholar 

  25. Saitoh O, Courchesne E, Egaas B, Lincoln A, Schreibman L. 1995. Crosssectional area of the posterior hippocampus in autistic patients with cerebellar and corpus callosum abnormalities. Neurology 45(2):317-324.

    Google Scholar 

  26. Manes F, Piven J, Vrancic D, Nanclares V, Plebst C, Starkstein S. 1999. An MRI study of the corpus callosum and cerebellum in mentally retarded autistic individuals. J Neuropsychiatry Clin Neurosci 11(4):470-474.

    Google Scholar 

  27. Chung M, Dalton K, Alexander A, Davidson R. 2004. Less white matter concentration in autism: 2D voxel-based morphometry. Neuroimage 23:242-251.

    Article  Google Scholar 

  28. Waiter G, Williams J, Murray A, Gilchrist A, Perrett D, Whiten A. 2005. Structural white matter deficits in high-functioning individuals with autistic spectrum disorder: a voxel-based investigation. Neuroimage 24(2):455-461.

    Article  Google Scholar 

  29. Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, Reiss A. 2004. White matter structure in autism: preliminary evidence from diffusion tensor imaging. Am J Biol Psychiatry 55:323-326.

    Article  Google Scholar 

  30. Vidal C, Nicolson R, DeVito T, Hayashi K, Geaga J, Drost D, Williamson P, Rajakumar N, Sui Y, Dutton R, Toga A, Thompson P. 2006. Mapping corpus callosum deficits in autism: an index of aberrant cortical connectivity. J Biol Psychiatry 60(3):218-225.

    Article  Google Scholar 

  31. Cody H, Pelphrey K, Piven J. 2002. Structural and functional magnetic resonance imaging of autism. Int J Dev Neurosci, 20:421-438.

    Article  Google Scholar 

  32. Williams R, Hauser S, Purpura D, DeLong G, Swisher C. 1980. Autism and mental retardation: neuropathologic studies performed in four retarded persons with autistic behavior. J Arch Neurol, 37(12):749-753.

    Google Scholar 

  33. Ritvo E, Freeman B, Scheibel A, Duong T, Robinson H, Guthrie D, Ritvo A. 1986. Lower Purkinje cell counts in the cerebella of four autistic subjects: initial findings of the UCLA- NSAC autopsy research report. Am J Psychiatry 143:862-866.

    Google Scholar 

  34. Lee M, Martin-Ruiz C, Graham A, Court J, Jaros E, Perry R, Iversen P, Bauman M, Perry E. 2002. Nicotinic receptor abnormalities in the cerebellar cortex in autism. Brain 125(7):1483-1495.

    Article  Google Scholar 

  35. Bailey A, Luthert P, Dean A, Harding B, Janota I, Montgomery M, Rutter M, Lantos P. 1998. A clinicopathological study of autism. Brain 121(5):889-905.

    Article  Google Scholar 

  36. Courchesne E, Yeung-Courchesne R, Press G, Hesselink J, Jernigan T. 1988. Hypoplasia of cerebellar vermal lobules VI and VII in autism. N Engl J Med 318(21):1349-1354.

    Article  Google Scholar 

  37. Allen G, Courchesne E. 2003. Autism and mental retardation: neuropathologic studies performed in four retarded persons with autistic behavior. Am J Psychiatry 160: 262-273.

    Article  Google Scholar 

  38. Mountcastle VB. 1997. The minicolumnar organization of the neocortex. Brain 120:701-722.

    Article  Google Scholar 

  39. Mountcastle VB. 2003. Introduction: computation in cortical columns. J Cereb Cortex 13(1):2-4.

    Article  Google Scholar 

  40. Casanova MF, Buxhoeveden D, Switala A, Roy E. 2002. Minicolumnar pathology in autism. Neurology 58:428-432.

    Google Scholar 

  41. Casanova MF, Buxhoeveden D, Switala A, Roy E. 2002. Neuronal density and architecture (gray level index) in the brains of autistic patients. J Child Neurol, 17:515-521.

    Article  Google Scholar 

  42. Casanova MF. 2004. White matter volume increases and minicolumns in autism. Ann Neurol 56(3):453.

    Article  Google Scholar 

  43. Casanova MF, Van Kooten I, Switala A, van Engeland H, Heinsen H, Steinbusch H, Hof PR, Trippe J, Stone J, Schmitz C. 2006. Minicolumnar abnormalities in autism. Acta Neuropathol. In press. Available online.

    Google Scholar 

  44. Herbert M, Ziegler D, Makris N, Filipek P, Kemper T, Normandin J, Sanders HA, Kennedy D, Caviness VJ. 2004. Localization of white matter volume increase in autism and developmental language disorder. Ann Neurol 55:530-540.

    Article  Google Scholar 

  45. Lainhart JE, Lazar M, Bigler E, Alexander A. 2005. The brain during life in autism: ad- vances in neuroimaging research. In Recent advances in autism research, pp. 57-108. Ed MF Casanova. New York: NOVA Biomedical.

    Google Scholar 

  46. Bauman ML, Kemper TL. 1990. Limbic and cerebellar abnormalities are also present in an autistic child of normal intelligence. Neurology 40:359.

    Google Scholar 

  47. Bauman ML, Kemper TL. 1994. Neuroanatomic observations of the brain in autism. In The neurobiology of autism, pp. 119-145. Ed ML Bauman, TL Kemper TL. Baltimore: Johns Hopkins UP.

    Google Scholar 

  48. Schumann CM, Buonocore HM, Amaral DG. 2001. Magnetic resonance imaging of the post- mortem autistic brain. J Autism Dev Disord 31(6):561-568.

    Article  Google Scholar 

  49. Osher S, Sethian J. 1988. Fronts propagating with curvature speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79:12-49.

    Article  MATH  MathSciNet  Google Scholar 

  50. Chan T, Sandberg B, Vese L. 2000. Active contours without edges for vector valued images. J Vis Commun Image Represent, 2:130-141.

    Article  Google Scholar 

  51. Zaho H-K, Chan T, Merriman B, Osher S. 1996. A variational level set approach to multiphase motion. J Comp Phys 127:179-195.

    Article  Google Scholar 

  52. Zeng X, Staib LH, Duncan JS. 1998. Volumetric layer segmentation using coupled surface propagation. In Proceedings of the IEEE conference on computer vision and pattern recog- nition (CVPR), pp. 179-195. Washington, DC: IEEE Computer Society.

    Google Scholar 

  53. Farag AA, El-Baz A, Gimelfarb G. 2004. Precise image segmentation by iterative em-based approximation of empirical gray level distribution with linear combination of gaussians. In IEEE international workshop on learning in computer vision and pattern recognition, pp. 121-129. Washington, DC: IEEE Computer Society.

    Google Scholar 

  54. Farag AA, Hassan H. 2004. Adaptive segmentation of multi-modal 3d data using robust level set techniques. In Proceedings of the 7th international conference on medical image computing and computer-assisted intervention (MICCAI’2004). Lecture notes in computer science, Vol. 2306, pp. 169-176. New York: Springer.

    Google Scholar 

  55. Goldenberg R, Kimmel R, Rivlin E, Rudzsky M. 2002. Cortex segmentation: a fast variational geometric approach. IEEE Trans Med Imaging 21(2):1544-1551.

    Article  Google Scholar 

  56. Geusebroek M, Burghouts G, Smeulders A. 2005. The Amsterdam library of object images. Int J Comput Vis 61(1):103-112.

    Article  Google Scholar 

  57. Cerveny V. 1995. Ray synthetic seismograms for complex two- and three-dimensional structures. Am J Geophys 58:2-26.

    Google Scholar 

  58. Vidale JE. 1990. Finite-difference calculation of traveltimes in three dimensions. Geophysics 55:521-526.

    Article  Google Scholar 

  59. Van Trier J, Symes WW. 1991. Upwind finite-difference calculation of traveltimes. Geophysics 56:812-821.

    Article  Google Scholar 

  60. Podvin P, and Lecomte I. 1991. Finite-difference computation of traveltimes in very contrasted velocity models: a massively parallel approach and its associated tools. Geophys J Int 105:271-284.

    Article  Google Scholar 

  61. Adalsteinsson D, and Sethian J. 1995. A fast level set method for propagating interfaces. J Comput Phys 118:269-277.

    Article  MATH  MathSciNet  Google Scholar 

  62. Kim S. 1999. ENO-DNO-PS: a stable, second-order accuracy eikonal solver. J Soc Explor Geophys 28:1747-1750.

    Google Scholar 

  63. Sethian J. 1999. Level sets methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, 2nd ed. Cambridge: Cambridge UP.

    Google Scholar 

  64. Kimmel R, and Sethian JA. 1998. Fast marching methods on triangulated domains. Proc Natl Acad Sci USA 95(11):8341-8435.

    MathSciNet  Google Scholar 

  65. Sethian JA, Vladimirsky A. 2000. Fast methods for the eikonal and related Hamilton-Jacobi equations on unstructured meshes. Proc Natl Acad Sci USA 97(11):5699-5703.

    Article  MATH  MathSciNet  Google Scholar 

  66. Hassouna MS, Farag AA. 2006. Accurate tracking of monotonically advancing fronts. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 355-362, Washington, DC: IEEE Computer Society.

    Google Scholar 

  67. Shen D, Davatzikos C. 2002. Hammer: hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imaging 21(11):257-270.

    Google Scholar 

  68. Zitova B, Flusser J. 2003. Image registration methods: a survey. Image Vision Comput 21(21):977-1000.

    Article  Google Scholar 

  69. Fahmi R., Farag A. A., El-Baz A. 2006. New deformable registration technique using scale space and curve evolution theory and a finite element based validation framework. In Proceed- ings of the 28th IEEE EMBS annual international conference, pp. 3041-3044. Washington, DC: IEEE Computer Society.

    Google Scholar 

  70. Lindeberg T. 1994. Scale-space theory in computer vision. New York: Kluwer Academic.

    Google Scholar 

  71. Mikolajczyk K, Schmid C. 2002. An affine invariant interest point detector. In Proceedings of the European conference on computer vision (ECCV’02). Lecture notes in computer science, Vol. 2350, pp. 128-142. Ed A Heyden. New York: Springer.

    Google Scholar 

  72. Abdel-Hakim A., Farag A. A. 2005. "CSIFT: a SIFT Descriptor with Color Invariant Char- acteristics", Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR-06), pp. 1978-1983, June 2006, Los Alamitos, CA, USA.

    Google Scholar 

  73. Mikolajczyk K, and Schmid C. 2005. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Machine Intell 27(10):1615-1630.

    Article  Google Scholar 

  74. Lowe D. 2004. Distinctive image features from scale-invariant key points. Int J Comput Vision 60(2):91-110.

    Article  Google Scholar 

  75. Rueckert D, Sonoda L, Hayes C, Hill D, Leach M, Hawkes D. 1999. Nonrigid registration using free-form deformations: application to breast mr images. IEEE Trans Med Imaging 18(8):712-721.

    Article  Google Scholar 

  76. Keller T, Kana R, Just M. 2006. A developmental study of the structural integrity of white matter in autism. NeuroReport. In press.

    Google Scholar 

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Authors and Affiliations

  1. Computer Vision and Image Processing Laboratory, University of Louisville, 40292, Louisville, KY, USA

    Professor Aly A. Farag

  2. Computer Vision and Image Processing Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, Kentucky, USA

    Rachid Fahmi, Alaa E. Abdel-Hakim & Hossam Abd El-Munim

  3. Department of Psychiatry and Behavioral Sciences, University of Louisville, Louisville, Kentucky, USA

    Manuel F. Casanova

  4. Bioengineering Department, University of Louisville, Louisville, Kentucky, USA

    Ayman El-Baz

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  1. Professor Aly A. Farag
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  5. Hossam Abd El-Munim
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  6. Ayman El-Baz
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Farag, A.A., Fahmi, R., Casanova, M.F., Abdel-Hakim, A.E., El-Munim, H.A., El-Baz, A. (2007). Robust Neuroimaging-Based Classification Techniques Of Autistic Vs. Typically Developing Brain. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68343-0_16

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