Academic Radiology
Volume 15, Issue 3 , Pages 300-313 , March 2008

Computer-Assisted Segmentation of White Matter Lesions in 3D MR Images Using Support Vector Machine

  • Zhiqiang Lao

      Affiliations

    • Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104
    • Corresponding Author InformationAddress correspondence to: Z.L.
    • ,
  • Dinggang Shen

      Affiliations

    • Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104
    • ,
  • Dengfeng Liu

      Affiliations

    • Lister Hill National Center for Biomedical Communications, National Library of Medicine/National Institute of Health, Bethesda, MD.
    • ,
  • Abbas F. Jawad

      Affiliations

    • Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104
    • Department of Biostatistics, Children’s Hospital of Philadelphia, Philadelphia, PA
    • ,
  • Elias R. Melhem

      Affiliations

    • Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104
    • ,
  • Lenore J. Launer

      Affiliations

    • Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD
    • ,
  • R. Nick Bryan

      Affiliations

    • Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104
    • ,
  • Christos Davatzikos

      Affiliations

    • Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104

Received 23 August 2007 ,Accepted 1 October 2007.

References 

  1. Prins ND, van Dijk EJ, den Heijer T, et al. Cerebral white matter lesions and the risk of dementia. Arch Neurol. 2004;61:1531–1534
  2. Snowdon DA, Greiner LH, Mortimer JA, et al. Brain infarction and the clinical expression of Alzheimer’s disease (The Nun study). JAMA. 1997;277:813–817
  3. Schneider JA, Wilson RS, Cochran EJ, et al. Relation of cerebral infarctions to dementia and cognitive function in older persons. Neurology. 2003;60:1082–1088
  4. Schneider JA, Wilson RS, Bienias JL, et al. Cerebral infarctions and the likelihood of dementia from Alzheimer disease pathology. Neurology. 2004;62:1148–1155
  5. Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med. 2003;348:1215–1222
  6. Gearing M, Mirra SS, Hedreen JC, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) (Part X. Neuropathology confirmation of the clinical diagnosis of Alzheimer’s disease). Neurology. 1995;45:461–466
  7. Ince P, Xuerb J, Mackenzie IR, et al. Neuropathology of a community sample of elderly demented and nondemented people. Brain Pathol. 2000;10:592–593
  8. Vermeer SE, Prins ND, den Heijer T, et al. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med. 2003;348:1215–1222
  9. Zekry D, Duyckaerts C, Moulias R, et al. Degenerative and vascular lesions of the brain have synergistic effects in dementia of the elderly. Acta Neuropathol. 2002;103:481–487
  10. Snowdon DA, Greiner LH, Mortimer JA, et al. Brain infarction and the clinical expression of Alzheimer disease. JAMA. 1997;277:813–817
  11. Schneider JA, Wilson RS, Bienias JL, et al. Cerebral infarctions and the likelihood of dementia from Alzheimer disease pathology. Neurology. 2004;13:1148–1155
  12. de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann Neurol. 2000;47:145–151
  13. de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and depressive symptoms in elderly adults. Arch Gen Psychiatry. 2000;57:1071–1076
  14. Longstreth WT, Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people (The Cardiovascular Health Study). Stroke. 1996;27:1274–1282
  15. Briley DP, Haroon S, Sergent S, et al. Does leukoaraiosis predict morbidity and mortality?. Neurology. 2000;54:90–94
  16. Allen KV, Frier BM, Strachan MWJ. The relationship between type 2 diabetes and cognitive dysfunction: longitudinal studies and their methodological limitations. Eur J Pharmacol. 2004;490:169–175
  17. Arvanitakis Z, Wilson RS, Bienias JL, et al. Diabetes mellitus and risk of Alzheimer disease and decline in cognitive function. Arch Neurol. 2004;61:661–666
  18. Luchsinger JA, Tang MX, Stern Y, et al. Diabetes mellitus and risk of Alzheimer’s disease and dementia with stroke in a multiethnic cohort. Am J Epidemiol. 2001;154:635–641
  19. Araki Y, et al. MRI of the brain in diabetes mellitus. Neuroradiology. 1994;36:101–103
  20. Eguchi K, Kario K, Shimada K. Greater impact of coexistence of hypertension and diabetes on silent cerebral infarcts. Stroke. 2003;34:2471–2474
  21. Heijer T, Vermeer SE, van Dijk , et al. Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI. Diabetologia. 2003;46:1604–1610
  22. Inoue T, Fushimi H, Yamada Y, et al. Asymptomatic multiple lacunae in diabetics and non-diabetics detected by brain magnetic resonance imaging. Diabetes Res Clin Pract. 1996;31:81–86
  23. Kario K, Ishikawa J, Hoshide S, et al. Diabetic brain damage in hypertension: role of renin-angiotensin system. Hypertension. 2005;45:887–893
  24. Schmidt R, Launer LJ, Nilsson LG, et al. Magnetic resonance imaging of the brain in diabetes: the Cardiovascular Determinants of Dementia (CASCADE) study. Diabetes. 2004;53:687–692
  25. Williamson J, Miller ME, Bryan RN, et al. The Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes Study (ACCORD-MIND): rationale, design, and methods. Am J Cardiol. 2007;99:112i–122i
  26. Mäntylä R, Erkinjuntti T, Salonen O, et al. Variable agreement between visual rating scales for white matter hyperintensities on MRI comparison of 13 rating scales in a poststroke cohort. Stroke. 1997;28:1614–1623
  27. Bryan RN, Manolio TA, Schertz LD, et al. A method for using MR to evaluate the effects of cardiovascular disease of the brain: the cardiovascular health study. Am J Neuroradiol. 1994;15:1625–1633
  28. De Groot JC, De Leeuw FE, Oudkerk M, et al. Periventricular cerebral white matter lesions predict rate of cognitive decline. Ann Neurol. 2002;52:335–341
  29. Benson RR, Guttmann CR, Wei X, et al. Older people with impaired mobility have specific loci of periventricular abnormality on MRI. Neurology. 2002;58:48–55
  30. Smith CD, Snowdon DA, Wang H, et al. White matter volumes and periventricular white matter hyperintensities in aging and dementia. Neurology. 2000;54:838–842
  31. Kamber M, Shingal R, Collins DL, et al. Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images. IEEE Trans Med Imaging. 1995;14:442–453
  32. Warfield S, Dengler J, Zaers J, et al. Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions. J Image Guided Surg. 1995;1:326–338
  33. Udupa J, Wei L, Samarasekera S, et al. Multiple sclerosis lesion quantification using fuzzy-connectedness principles. IEEE Trans Med Imaging. 1997;16:598–609
  34. Welti D, Gerig G, Radu EW, et al. Spatio-temporal segmentation and characterization of active multiple sclerosis lesions in serial MRI data. Proc Information Proc Med Imaging. 2001;438–445
  35. Zijdenbos AP, Dawant BM, Margolin RA, et al. Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging. 1994;13:716–724
  36. Alfano B, Brunetti A, Larobina M, et al. Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis. J Magn Reson Imaging. 2000;12:799–807
  37. Van Leemput K, Maes F, Vandermeulen D, et al. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging. 2001;20:677–688
  38. Udupa JK, Nyúl LG, Ge Y, et al. Multiprotocol MR image segmentation in multiple sclerosis: experience with over 1,000 studies. Acad Radiol. 2001;8:1116–1126
  39. Van Leemput K, Maes F, Vandermeulen D, et al. Automated segmentation of multiple sclerosis lesions my model outlier detection. 2000;KU-Leuven
  40. Wu Y, Warfield SK, Tan IL, et al. Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI. NeuroImage. 2006;32:1205–1215
  41. Zijdenbos AP, Forghani R, Evans AC. Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans MedImaging. 2002;21:1280–1291
  42. Warfield SK, Kaus M, Jolesz FA, et al. Adaptive, template moderated, spatially varying statistical classification. Med Image Anal. 2000;4:43–55
  43. Wei X, Warfield SK, Zou KH, et al. Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy. J Magn Reson Imaging. 2002;15:203–209
  44. Anbeek P, Vincken KL, van Osch MJ, et al. Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage. 2004;21:1037–1044
  45. Admiraal-Behloul F, van den Heuvel DM, Olofsen H, et al. Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. NeuroImage. 2005;28:607–617
  46. Yu S, Pham DL, Shen D, et al. Automatic segmentation of white matter lesions in T1-weighted brain MR images. IEEE International Symposium on Biomedical Imaging; Macro to Nano. 2001;253–256
  47. Anbeek P, Vincken KL, van Bochove GS, et al. Probabilistic segmentation of brain tissue in MR imaging. NeuroImage. 2005;27:795–804
  48. Gerig G, Welti D, Guttmann CR, et al. Exploring the discrimination power of the time domain for segmentation and characterization of lesions in serial MR data. Med Image Anal. 2000;4:31–42
  49. Meier DS, Guttmann CRG. MRI time series modeling of MS lesion development. NeuroImage. 2006;32:531–537
  50. Jack CRJ, O’Brien PC. FLAIR histogram segmentation for measurement of leukoaraiosis volume. J Magn Res Imaging. 2001;14:668–676
  51. Mohamed FB, Vinitski S, Gonzalez CF, et al. Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary results. Magn Reson Imaging. 2001;19:207–218
  52. Viola P, Wells WM. Alignment by maximization of mutual information. Proc Int Conf Computer Vision. 1995;Los Alamitos, CA
  53. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage. 2004;23:208–219
  54. Smith SM. Fast robust automated brain extraction. Human Brain Mapping. 2002;17:143–155
  55. Sled J, Zijdenbos A, Evans A. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17:87–97
  56. Gawne-Cain ML, O’Riordan JI, Coles A, et al. MRI lesion volume measurement in multiple sclerosis and its correlation with disability: a comparison of fast fluid attenuated inversion recovery (fFLAIR) and spin echo sequences. J Neurol Neurosurg Psychiatry. 1998;64:197–203
  57. Bastianello S, Bozzao A, Paolillo A, et al. Fast spin-echo and fast fluid-attenuated inversion-recovery versus conventional spin-echo sequences for MR quantification of multiple sclerosis lesions. Am J Neuroradiol. 1997;18:699–704
  58. Rovaris M, Comi G, Rocca MA, et al. Relevance of hypointense lesions on fast fluid-attenuated inversion recovery MR images as a marker of disease severity in cases of multiple sclerosis. Am J Neuroradiol. 1999;20:813–820
  59. Bakshi R, Caruthers SD, Janardhan V, et al. Intraventricular CSF pulsation artifact on fast fluid-attenuated inversion-recovery MR images: analysis of 100 consecutive normal studies. Am J Neuroradiol. 2000;21:503–508
  60. Tanaka N, Abe T, Kojima K, et al. Applicability and advantages of flow artifact–insensitive fluid-attenuated inversion-recovery MR sequences for imaging the posterior fossa. Am J Neuroradiol. 2000;21:1095–1098
  61. Vapnik VN. Statistical learning theory. New York, NY: Wiley; 1998;
  62. Vapnik VN. The nature of statistical learning theory (statistics for engineering and information science). 2nd ed.. New York, NY: Springer-Verlag; 1999;
  63. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery. 1998;2:121–167
  64. Vapnik VN. Statistical learning theory. New York, NY: Wiley; 1998;
  65. Lao Z, Shen D, Xue Z, et al. Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage. 2004;21:46–57
  66. Duda RO, Hart PE, Stork DG. Pattern classification. New York, NY: John Wiley and Sons, Inc; 2001;
  67. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–577

1 Supported (in part) by the Intramural Research Program of the NIH, National Institute of Aging contract N01-HC-95178. Image analysis was supported in part by R01-AG-1497.

PII: S1076-6332(07)00583-1

doi: 10.1016/j.acra.2007.10.012

Academic Radiology
Volume 15, Issue 3 , Pages 300-313 , March 2008

Article Tools

* Image Usage & Resolution