Neuroradiology

, Volume 54, Issue 4, pp 299–320

Segmentation of multiple sclerosis lesions in MR images: a review

  • Daryoush Mortazavi
  • Abbas Z. Kouzani
  • Hamid Soltanian-Zadeh
Diagnostic Neuroradiology

Abstract

Introduction

Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease.

Methods

For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past.

Results

This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques.

Conclusions

Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.

Keywords

Multiple sclerosis Magnetic resonance imaging Segmentation Image processing Pattern recognition 

References

  1. 1.
    Oseworthy JHN, Cchinetti LC, Odriguez MR, Einshenker BGW (2000) Multiple sclerosis. N Engl J Med 343(13):938–952CrossRefGoogle Scholar
  2. 2.
    Filippi M, Horsfield MA, Tofts PS, Barkhof F, Thompson AJ et al (1995) Quantitative assessment of MRI lesion load in monitoring the evolution of multiple sclerosis. Brain 118(6):1601–1612PubMedCrossRefGoogle Scholar
  3. 3.
    Tedeschi G, Lavorgna L, Russo P, Prinster A, Dinacci D et al (2005) Brain atrophy and lesion load in a large population of patients with multiple sclerosis. Neurology 65(2):280–285PubMedCrossRefGoogle Scholar
  4. 4.
    Nusbaum AO, Tang CY, Wei T-C, Buchsbaum MS, Atlas SW (2000) Whole-brain diffusion MR histograms differ between MS subtypes. Neurology 54(7):1421–1427PubMedGoogle Scholar
  5. 5.
    Horsfield MA, Rovaris M, Rocca MA, Rossi P, Benedict RH et al (2003) Whole-brain atrophy in multiple sclerosis measured by two segmentation processes from various MRI sequences. J Neurol Sci 216(1):169–177PubMedCrossRefGoogle Scholar
  6. 6.
    van den Elskamp IJ, Boden B, Dattola V, Knol DL, Filippi M et al (2010) Cerebral atrophy as outcome measure in short-term phase 2 clinical trials in multiple sclerosis. Neuroradiology 52:875–881PubMedCrossRefGoogle Scholar
  7. 7.
    Quarantelli M, Ciarmiello A, Morra VB, Orefice G, Larobina M et al (2003) Brain tissue volume changes in relapsing-remitting multiple sclerosis: correlation with lesion load. Neuroimage 18(2):360–366PubMedCrossRefGoogle Scholar
  8. 8.
    Pagani ERM, Gallo A, Rovaris M, Martinelli V, Comi G (2005) Regional brain atrophy evolves differently in patients with multiple sclerosis according to clinical phenotype. Am J Neuroradiol 26:341–346PubMedGoogle Scholar
  9. 9.
    Horakova D, Cox JL, Havrdova E, Hussein S, Dolezal O et al (2008) Evolution of different MRI measures in patients with active relapsing–remitting multiple sclerosis over 2 and 5 years. A case control study. J Neurol Neurosurg Psychiatry 79(4):407–414PubMedCrossRefGoogle Scholar
  10. 10.
    Fisher E, Lee JC, Nakamura K, Rudick RA (2008) Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann Neurol 64(3):255–265PubMedCrossRefGoogle Scholar
  11. 11.
    Fisniku LK, Chard DT, Jackson JS, Anderson VM, Altmann DR et al (2008) Gray matter atrophy is related to long-term disability in multiple sclerosis. Ann Neurol 64(3):247–254PubMedCrossRefGoogle Scholar
  12. 12.
    Magraner MJ, Bosca I, Simó-Castelló M, García-Martí G, Alberich-Bayarri A, et al. (2011) Brain atrophy and lesion load are related to CSF lipid-specific IgM oligoclonal bands in clinically isolated syndromes. Neuroradiology doi:10.1007/s00234-00011-00841-00237
  13. 13.
    Khayati R, Vafadust M, Towhidkhah F, Nabavi SM (2008) A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Comput Med Imaging Graph 32(2):124–133PubMedCrossRefGoogle Scholar
  14. 14.
    Helms G, Piringer A (2003) T2-based segmentation of periventricular paragraph sign volumes for quantification of proton magnetic paragraph sign resonance spectra of multiple sclerosis lesions. Magma 16(1):10–16PubMedCrossRefGoogle Scholar
  15. 15.
    Bozzali M, Cercignani M, Sormani MP, Comi G, Filippi M (2002) Quantification of brain gray matter damage in different MS phenotypes by use of diffusion tensor MR imaging. Am J Neuroradiol 23:985–988PubMedGoogle Scholar
  16. 16.
    Trapp BD, Nave KA (2008) Multiple sclerosis: an immune or neurodegenerative disorder? Annu Rev Neurosci 31:247–269PubMedCrossRefGoogle Scholar
  17. 17.
    Sahraian MA, Radue EW (2008) MRI Atlas of MS Lesions. Springer, BerlinGoogle Scholar
  18. 18.
    Okuda T, Korogi Y, Shigematsu Y, Sugahara T, Hirai T et al (1999) Brain lesions: when should fluid-attenuated inversion-recovery sequences be used in MR evaluation? Radiology 212:793–798PubMedGoogle Scholar
  19. 19.
    Wicks DAG, Toffs PS, Miller DH, Boulay GH, Feinstein A et al (1992) Volume measurement of multiple sclerosis lesions with magnetic resonance images. Neuroradiology 34(6):475–479PubMedCrossRefGoogle Scholar
  20. 20.
    Molyneux PD, Wang L, Lai MJG, Tofts PS, Moseley IF et al (1998) Quantitative techniques for lesion load measurement in multiple sclerosis: an assessment of the global threshold technique after non uniformity and histogram matching corrections. Eur J Neurol 5(1):55–60PubMedCrossRefGoogle Scholar
  21. 21.
    Molyneux PD, Tofts PS, Fletcher A, Gunn B, Robinson P et al (1998) Precision and reliability for measurement of change in MRI lesion volume in multiple sclerosis: a comparison of two computer assisted techniques. J Neurol Neurosurg Psychiatry 65:42–47PubMedCrossRefGoogle Scholar
  22. 22.
    Goldberg-Zimring D, Achiron A, Miron S, Faibel M, Azhari H (1998) Automated detection and characterization of multiple sclerosis lesions in brain MR images. J Magn Reson Imaging 16(3):311–318CrossRefGoogle Scholar
  23. 23.
    Warfield SK, Wu Y, Tan IL, Wells WM, Meier DS et al (2006) Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI. Neuroimage 32(3):1205–1215PubMedCrossRefGoogle Scholar
  24. 24.
    Stamatakis EA, Tyler LK (2005) Identifying lesions on structural brain images—validation of the method and application to neuropsychological patients. Brain Lang 94(2):167–177PubMedCrossRefGoogle Scholar
  25. 25.
    Anbeek P, Vincken KL, Van Osch MJ, Bisschops RH, Van Der Grond J (2004) Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 21(3):1037–1044PubMedCrossRefGoogle Scholar
  26. 26.
    Anbeek P, Vincken KL, Van OMJ, Bisschops RH, Van Der Grond J (2004) Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 8(3):205–215PubMedCrossRefGoogle Scholar
  27. 27.
    Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC (1994) Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imag 13(4):716–724CrossRefGoogle Scholar
  28. 28.
    Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ et al (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17(3):463–469PubMedCrossRefGoogle Scholar
  29. 29.
    Simmons A, Tofts PS, Barker GJ, Arridge SR (1994) Sources of intensity nonuniformity in spin echo images at 1.5 T. Magn Reson Med 32(1):121–128PubMedCrossRefGoogle Scholar
  30. 30.
    Sled JG, Pike GB (1998) Understanding intensity non-uniformity inMRI. MICCAI 98(1496):614–622Google Scholar
  31. 31.
    Van Leemput K (2001) Quantitative analysis of signal abnormalities in MR imaging for multiple sclerosis and Creutzfeldt–Jakob disease. Ph.D. thesis, K.U. Leuven, Leuven, BelgiumGoogle Scholar
  32. 32.
    Johnston B, Atkins MS, Mackiewich B, Anderson M (1996) Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Trans Med Imag 15(2):154–169CrossRefGoogle Scholar
  33. 33.
    Mohamed FB, Vinitski S, Gonzalez CF, Faro S, Burnett C et al (1995) Image non-uniformity correction in high field (1.5 T) MRI. Proc IEEE Eng Med Biology 17:36–37Google Scholar
  34. 34.
    Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imag 18(1):87–97CrossRefGoogle Scholar
  35. 35.
    Nyúl LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. IEEE Trans Med Imag 19(2):143–150CrossRefGoogle Scholar
  36. 36.
    Gerig G, Kubler O, Kikinis R, Jolesz FA (1992) Nonlinear anisotropic filtering of MRI data. IEEE Trans Med Imag 11(2):221–232CrossRefGoogle Scholar
  37. 37.
    Nissanov J, Madi S, Vinitski S (1997) Distance-based subset alignment of MR images. Radiology 205:51Google Scholar
  38. 38.
    Ekenel HK, Stiefelhagen R (2005) Local appearance based face recognition using discrete cosine transform. EUSIPCO 2005. Antalya, TurkeyGoogle Scholar
  39. 39.
    Ghazel M, Traboulsee A, Ward RK (2006) Semi-automated segmentation of multiple sclerosis lesions in brain MRI using texture analysis. In: IEEE Int Symp on Signal Processing and Information Technology. pp 6–10Google Scholar
  40. 40.
    Ghazel M, Traboulsee A, Ward RK (2006) Optimal filter design for multiple sclerosis lesions segmentation from regions of interest in brain MRI. In: IEEE Int Symp on Signal Processing and Information Technology. pp 1–5Google Scholar
  41. 41.
    Wang (1998) Correction for variations in MRI scanner sensitivity in brain studies with histogram matching. Magn Reson Med 39(2):322–327PubMedCrossRefGoogle Scholar
  42. 42.
    Wicks DAG, Barker GJ, Toffs PS (1993) Correction of intensity non uniformity in MR images in any orientation. Magn Reson Imaging 11(2):183–196PubMedCrossRefGoogle Scholar
  43. 43.
    Grimaud J, Lai M, Thorpe J, Adeleine P, Wang L et al (1996) Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. J Magn Reson Imaging 14(5):495–505CrossRefGoogle Scholar
  44. 44.
    Losseff NA, Wang L, Lai HM, Yoo DS, Gawne-Cain ML et al (1996) Progressive cerebral atrophy in multiple sclerosis a serial MRI study. Brain 119(6):2009–2019PubMedCrossRefGoogle Scholar
  45. 45.
    Hojjatoleslami SA, Kruggel F, DY VC (1999) Segmentation of white matter lesions from volumetric MR images. MICCAI’99, LNCS 1679. Springer, Berlin. pp 52–62Google Scholar
  46. 46.
    Pachai C, Zhu YM, Grimaud J, Hermier M, Dromigny-Badin A et al (1998) A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI. Comput Med Imaging Graph 22(5):399–408PubMedCrossRefGoogle Scholar
  47. 47.
    Mohamed FB, Vinitski S, Gonzalez CF, Faro SH, Lublin FA et al (2001) Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary results. Magn Reson Imaging 19(2):207–218PubMedCrossRefGoogle Scholar
  48. 48.
    Mohamed FB, Vinitski S, Faro SH, Gonzalez CF, Mack J et al (1999) Optimization of tissue segmentation of brain MR images based on multispectral 3D feature maps. J Magn Reson Imaging 17(3):403–409CrossRefGoogle Scholar
  49. 49.
    Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imag 16(2):187–198CrossRefGoogle Scholar
  50. 50.
    Boer R, Van Der Lijn F, Vrooman HA, Vernooij MW, Ikram MA, et al. (2007) Automatic segmentation of brain tissue and white matter lesions in MRI. In: Proceedings of IEEE International Symposium on Biomedical Imaging, WashingtonGoogle Scholar
  51. 51.
    Kamber M, Shinghal R, Collins DL, Francis GS, Evans AC (1995) Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images. IEEE Trans Med Imag 14(3):442–453CrossRefGoogle Scholar
  52. 52.
    Warfield S, Dengler J, Zaers J, Guttmann CRG, Wells WM et al (1995) Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions. J Image Guid Surg 1(6):326–338PubMedCrossRefGoogle Scholar
  53. 53.
    Nett JM (2001) The study of MS using MRI, image processing, and visualization. M.Sc. thesis, Louisville UniversityGoogle Scholar
  54. 54.
    Wells WM, Grimson WEL, Kikinis R, Jolesz FA (1996) Adaptive segmentation of MRI data. IEEE Trans Med Imag 15(4):429–442CrossRefGoogle Scholar
  55. 55.
    Kikinis R, Shenton ME, Gerig G, Martin J, Anderson M et al (1992) Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. J Magn Reson Imaging 2(6):619–629PubMedCrossRefGoogle Scholar
  56. 56.
    Wei X, Warfield SK, Zou KH, Wu Y, Ki X et al (2002) Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy. J Magn Reson Imaging 15(2):203–209PubMedCrossRefGoogle Scholar
  57. 57.
    Leemput KV, Maes F, Vandermeulen D, Colchester A, Suetens P (2001) Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imag 20(8):677–688CrossRefGoogle Scholar
  58. 58.
    Dugas-Phocion G, Gonzalez MA, Lebrun C, Chanalet S, Bensa C, et al. (2004) Hierarchical segmentation of multiple sclerosis lesions in multi sequence MRI. In: IEEE international symposium on biomedical imaging: nano to macro, 1. pp 157–160Google Scholar
  59. 59.
    Harmouche R, Collins L, Arnold D, Francis S, Arbel T (2006) Bayesian MS lesion classification modeling regional and local spatial information. In: The 18th international conference on pattern recognition (ICPR'06). pp 984–987Google Scholar
  60. 60.
    Bricq S, Collet Ch, Armspach JP (2008) Markovian segmentation of 3D brain MRI to detect multiple sclerosis lesions. In: IEEE international conference on image processing, ICIP'2008. pp 733–736Google Scholar
  61. 61.
    Neykov N, Neytchev P (1990) A robust alternative of the of the Maximum Likelihood Estimator. COMPSTAT 1990—Short Communications. Dubrovnik, Yugoslavia. pp 99–100Google Scholar
  62. 62.
    Prastawa M, Gerig G (2008) Automatic MS lesion segmentation by outlier detection and information theoretic region partitioning. Scientific Computing and Imaging Insttute, Utah UniversityGoogle Scholar
  63. 63.
    Khayati R, Vafadust M, Towhidkhah F, Nabavi M (2008) Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model. Comput Biol Med 38(3):379–390PubMedCrossRefGoogle Scholar
  64. 64.
    Martinez WL, Martinez AR (2002) Computational statistics handbook with MATLAB. Chapman & Hall, Boca RatonGoogle Scholar
  65. 65.
    Ait-Ali LS, Prima S, Hellier P, Carsin B, Edan G, et al. (2005) STREM: a robust multidimensional parametric method to segment MS lesions in MRI. MICCAI 2005 LNCS 3749. pp 409–416Google Scholar
  66. 66.
    Garcia-Lorenzo D, Prima S, Morrissey SP, Barillot C (2008) A robust expectation-maximization algorithm for multiple sclerosis lesion segmentation. MICCAI 2008 inserm-00421713, version1. pp 1–9Google Scholar
  67. 67.
    Neykov N, Filzmoser P, Dimova R, Neytchev P (2007) Robust fitting of mixtures using the trimmed likelihood estimator. Comput Stat Data Anal 52(1):299–308CrossRefGoogle Scholar
  68. 68.
    Freifeld O, Greenspan H, Goldberger J (2009) Multiple sclerosis lesion detection using constrained GMM and curve evolution. Int J of Biomedical Imaging 2009:1–13CrossRefGoogle Scholar
  69. 69.
    Warfield S (1996) Fast k-NN classification for multichannel image data pattern recognition. Pattern Recognit Lett 17(7):713–721CrossRefGoogle Scholar
  70. 70.
    Liu J, Smith CD, Chebrolu H (2009) Automatic multiple sclerosis detection based on integrated square estimation. In: IEEE computer society conference on computer vision and pattern recognition workshops, Miami, FL. pp 31–38Google Scholar
  71. 71.
    Sajja BR, Datta S, He R, Mehta M, Gupta RK et al (2006) Unified approach for multiple sclerosis lesion segmentation on brain MRI. Ann Biomed Eng 34(1):142–151PubMedCrossRefGoogle Scholar
  72. 72.
    Sajja BR, Datta S, He R, Narayana PA (2004) A unified approach for lesion segmentation on MRI of multiple sclerosis. In: IEMBS'04, 26th Annual Int Conf IEEE: 1778–1881.Google Scholar
  73. 73.
    Datta S, Sajja BR, He R, Wolinsky JS, Gupta RK et al (2006) Segmentation and quantification of black holes in multiple sclerosis. Neuroimage 29(2):467–474PubMedCrossRefGoogle Scholar
  74. 74.
    Lao Z, Shen D, Liu D, Jawad AF, Melhem ER et al (2008) Computer-assisted segmentation of white matter lesions in 3D MR images, using support vector machine. Acad Radiol 15(3):300–313PubMedCrossRefGoogle Scholar
  75. 75.
    Viola P, William M III (1997) Alignment by maximization of mutual information. Int J Comput Vision 24(2):137–154CrossRefGoogle Scholar
  76. 76.
    Smith SM (2008) BET: brain extraction tool. FMRIB technical report. p. 1–25Google Scholar
  77. 77.
    Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New YorkGoogle Scholar
  78. 78.
    Lim KO, Pfefferbaum A (1989) Segmentation of MR brain images into cerebrospinal fluid spaces, white and grey matter. J Comput Assist Tomogr 13(4):588–593PubMedCrossRefGoogle Scholar
  79. 79.
    Dawant BM, Zijdenbos AP, Margolin RA (1993) Correction of intensity variations in MR images for computer-aided tissue classification. IEEE Trans Med Imag 12(4):770–781CrossRefGoogle Scholar
  80. 80.
    Zijdenbos AP, Forghani R, Evans AC (2002) Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imag 21(10):1280–1291CrossRefGoogle Scholar
  81. 81.
    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(2):192–205PubMedCrossRefGoogle Scholar
  82. 82.
    Udupa JK, Wei L, Samarasekera S, Miki Y, Van Buchem MA et al (1997) Multiple sclerosis lesion quantifiction using fuzzy connectedness principles. IEEE Trans Med Imag 16(5):598–609CrossRefGoogle Scholar
  83. 83.
    Udupa JK, Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Models Image Process 58(3):246–261CrossRefGoogle Scholar
  84. 84.
    Admasu F, Al-Zubi S, Toennies K, Bodammer N, Hinrichs H (2003) Segmentation of multiple sclerosis lesions from MR brain images using the principles of fuzzy-connectedness and artificial neuron networks. ICIP 2003. Int Conf on Image Processing 3:1081–1084Google Scholar
  85. 85.
    Boudraa AO, Dehakb SMR, Zhu YM, Pachai C, Bao YG et al (2000) Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering. Comput Biol Med 30(1):23–40PubMedCrossRefGoogle Scholar
  86. 86.
    Ardizzone E, Pirrone R, Gambino O, Peri D (2002) Two channels fuzzy c-means detection of multiple sclerosis lesions in multispectral MR images. In: Int Conf on Image Processing. pp 345–348Google Scholar
  87. 87.
    Kawa J, Pietka E (2007) Kernelized fuzzy c-means method in fast segmentation of demyelination plaques in multiple sclerosis. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS. pp 5616–5619Google Scholar
  88. 88.
    Admiraal-Behloul F, Van den Heuvel DMJ, Olofsen H, Van Osch MJP, Van der Grond J et al (2005) Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. Neuroimage 28(3):607–617PubMedCrossRefGoogle Scholar
  89. 89.
    Woods RP, Grafton ST, Watson JDG, Sicotte NL, Mazziotta JC (1998) Automated image registration II. General methods and intrasubject validation of linear and nonlinear models. J Comput Assist Tomogr 22(1):153–165PubMedCrossRefGoogle Scholar
  90. 90.
    Horsfield MA, Bakshi R, Rovaris M, Rocca MA, Dandamudi VS et al (2007) Incorporating domain knowledge into the fuzzy connectedness framework: application to brain lesion volume estimation in multiple sclerosis. IEEE Trans Med Imag 26(12):1670–1680CrossRefGoogle Scholar
  91. 91.
    Blonda P, Satalino G, Baraldi A, De Blasi R (1998) Segmentation of multiple sclerosis lesions in MRI by fuzzy neural networks: FLVQ and FOSART. In: Conf of the North American Fuzzy Information Processing Society-NAFIPS. pp 39–43Google Scholar
  92. 92.
    Bezdek JC (1981) In: Kluwe (ed) Pattern recognition with fuzzy object function algorithms. Academic, NorwellGoogle Scholar
  93. 93.
    Yu S, Pham D, Shen D, Herskovits EH, Resnick SM, et al. (2002) Automatic segmentation of white matter lesions in T1-weighted brain MR images. In: Symposium on Biomedical Imaging, Arlington Virginia, USA. pp 1–5Google Scholar
  94. 94.
    Pham DL, Prince JL (1999) Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imag 18(9):737–752CrossRefGoogle Scholar
  95. 95.
    Shen D, Davatzikos C (2002) HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imag 21(11):1421–1439CrossRefGoogle Scholar
  96. 96.
    Shen S, Szameitat AJ, Sterr A (2008) Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location a 3-D automatic approach. IEEE Trans Inf Technol Biomed 12(4):532–540PubMedCrossRefGoogle Scholar
  97. 97.
    Bazin PL, Pham DL (2008) Homeomorphic brain image segmentation with topological and statistical atlases. Med Image Anal 12(5):616–625PubMedCrossRefGoogle Scholar
  98. 98.
    Sethian JA (1999) Level set methods and fast marching methods. Cambridge University Press, New YorkGoogle Scholar
  99. 99.
    Shiee N, Bazin PL, Ozturk A, Reich DS, Calabresi PA et al (2010) A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49(2):1524–1535PubMedCrossRefGoogle Scholar
  100. 100.
    Thirion JP, Calmon G (1999) Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences. IEEE Trans Med Imag 18(5):429–441CrossRefGoogle Scholar
  101. 101.
    Thirion JP (1996) New feature points based on geometric invariants for 3D image registration. Int J Comput Vis 18(2):121–137CrossRefGoogle Scholar
  102. 102.
    Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Villemure JG et al (2004) Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans Med Imag 23(10):1301–1314CrossRefGoogle Scholar
  103. 103.
    Cuisenaire O, Thiran J-P, Macq B, Michel C, Volder AD, et al. (1996) Automatic registration of 3D MR images with a computerized brain atlas. In: IEEE SPIE Medical Imaging, Lecture Notes in Computer Science 2710. pp 438–448Google Scholar
  104. 104.
    Yang F, Jiang T, Zhu W, Kruggel F (2004) White matter lesion segmentation from volumetric MR images. Medical Imaging and Augmented Reality 3150:113–120CrossRefGoogle Scholar
  105. 105.
    Vrooman HA, Cocoscoc CA, van der Lijn F, Stokking R, Ikramd MA et al (2007) Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification. Neuro Image 37(1):71–81PubMedGoogle Scholar
  106. 106.
    Geremia E, Menze BH, Clatz O, Konukoglu E, Criminisi A et al (2010) (2010) Spatial decision forests for MS lesion segmentation in multi-channel MR images. MICCAI 13(Pt 1):111–118PubMedGoogle Scholar
  107. 107.
    Chagla GH, Busse RF, Sydnor R, Rowley HA, Turski PA (2008) Three-dimensional fluid attenuated inversion recovery imaging with isotropic resolution and nonselective adiabatic inversion provides improved three-dimensional visualization and cerebrospinal fluid suppression compared to two-dimensional flair at 3 Tesla. Investig Radiol 43(8):547–551CrossRefGoogle Scholar
  108. 108.
    Barker GJ (1998) 3D fast flair: a CSF-nulled 3D fast spin-echo pulse sequence. Magn Reson Imaging 16(7):715–720PubMedCrossRefGoogle Scholar
  109. 109.
    Tan IL, Pouwels PJW, van Schijndel RA, Adèr HJ, Manoliu RA et al (2002) Isotropic 3D fast FLAIR imaging of the brain in multiple sclerosis patients: Initial experience. Eur Radiol 12(3):559–567PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Daryoush Mortazavi
    • 1
  • Abbas Z. Kouzani
    • 1
  • Hamid Soltanian-Zadeh
    • 2
    • 3
    • 4
  1. 1.School of EngineeringDeakin UniversityGeelongAustralia
  2. 2.Image Analysis Laboratory, Radiology DepartmentHenry Ford Health SystemDetroitUSA
  3. 3.Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer EngineeringUniversity of TehranTehranIran
  4. 4.School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM)TehranIran

Personalised recommendations