Statistical approach to segmentation of single-channel cerebral mr images

IEEE Transactions on Medical Imaging

Volumn 16, Issue 2, 1997, Pages 176-186

  Rajapakse, Jagath C ^a   Giedd, Jay N ^a   Rapoport, Judith L ^a  

^a NATIONAL INSTITUTE OF MENTAL HEALTH   (United States)

Author keywords

Cerebrospinal fluid; Finite gaussian mixture; Gray matter; Magnetic resonance imaging; Markov random field; White matter

Indexed keywords

ADOLESCENT; ALGORITHM; ARTICLE; BRAIN; CHILD; COMPARATIVE STUDY; HISTOLOGY; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; STATISTICAL MODEL; STATISTICS;

ADOLESCENT; ALGORITHMS; BRAIN; CHILD; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; PHANTOMS, IMAGING;

ALGORITHMS; IMAGE SEGMENTATION; ITERATIVE METHODS; MAGNETIC FIELDS; MARKOV PROCESSES; SPURIOUS SIGNAL NOISE; STATISTICAL METHODS;

CEREBROSPINAL FLUID (CSF); FINITE GAUSSIAN MIXTURE; ITERATIVE CONDITIONAL MODE (ICM); MARKOV RANDOM FIELD (MRF);

MAGNETIC RESONANCE IMAGING;

EID: 0031106955     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/42.563663     Document Type: Article

References (91)

1. C. R. Jack Brain and cerebrospinal fluid volume: Measurement with MR imaging Radial vol. 178 pp. 22-24 1991.
2. R. B. Zipursky K. O. Lim E. V. Sullivan B. W. Brown and A. Pfefferbaum Widespread cerebral gray matter volume deficits in schizophrenia Arch. Gen. Psych. vol. 49 pp. 195-205 1992.
3. T. E. Schlaepfer G. J. Harris A. Y. Tien L. W. Peng S. Lee E. B. Federman G. A. Chase P. E. Barta and G. D. Pearlson Decreased regional cortical gray matter volume in schizophrenia Amer. J. Psych. vol. 151 no. 6 pp. 842-848 1994.
4. A. Pfefferbaum K. O. Lim R. B. Zipursky D. H. Mathalon M. J. Rosenbloom B. Lane C. N. Ha and E. V. Sullivan Brain gray and white matter volume loss accelerates with aging in chronic alcoholics: A quantitative MRI study Alcoholism Clin. Exp. Res. vol. 16 no. 6 pp. 1078-1089 1992.
5. W. L. Olsen F. M. Longo C. M. Mills and D. Norman White matter disease in AIDS: Finding at MR imaging Neuroradiol. vol. 169 pp. 445-448 1988.
6. H. Rusinek M. J. de Leon A. E. George L. A. Stylopoulos R. Chandra G. Smith T. Rand M. Mourino and H. Kowalski Alzheimer disease: Measuring loss of cerebral gray matter with MR imaging Neuroradiol. vol. 178 pp. 109-114 1991.
7. K. Fitzgerald Medical electronics IEEE Spectrum vol. 28 No. 1 pp. 76-78 1991.
8. R. A. Robb Three-Dimensional Biomédical Imaging. New York: VCH 1995.
9. M. E. Brummer R. M. Mersereau R. L. Eisner and R. R. J. Lewine Automatic detection of brain contours in MRI data sets IEEE Trans. Med. Imag. vol. 12 no. 2 pp. 153-166 1993.
10. A. K. H. Miller R. L. Alston and J. A. N. Corsellis Variation with age in the volumes of gray and white matter in the cerebral hemispheres of man: Measurement with an image analyzer Neuropathol. Appl. Neurobiol. vol. 6 pp. 119-132 1980.
11. J. C. Rajapakse J. N. Giedd C. DeCarli J. W. Snell A. McLaughlin Y. C. Vauss A. L. Krain S. D. Hamburger and J. L. Rapoport A technique for single-channel MR brain tissue segmentation: Application to a pédiatrie study Magn. Reson. Imag. vol. 18 no. 4 1996.
12. T. Autti R. Raininko S. L. Vanhanen M. Kallio and P. Santavuori MRI of the normal brain from early childhood to middle age Neuroradiol vol. 36 pp. 644-648 1994.
13. K. O. Lim R. B. Zipursky M. C. Watts and A. Pfefferbaum Decreased gray matter in normal aging: An in vivo magnetic resonance study J. Gerontol.: Biolog. Sci. vol. 47 no. 1 pp. B26-30 1992.
14. D. G. M. Murphy C. DeCarli M. B. Schapiro S. I. Rapoport and B. Horwitz Age-related differences in volumes of subcortical nuclei brain matter and cerebrospinal fluid in healthy men as measured with magnetic resonance imaging Arch. Neural. vol. 49 pp. 839-845 1992.
15. X. Hu N. Alperin D. N. Levin K. K. Tan and M. Mengeot Visualization of MR angiographie data with segmentation and volume-rendering techniques J. Magn. Reson. Imag. vol. 1 pp. 539-546 1991.
16. D. N. Levin X. Hu K. K. Tan S. Galhotra C. A. Pelizzari G. T. Y. Chen R. N. Beck C. T. Chen M. D. Cooper J. F. Mullan J. Hekmatpanah and J. P. Spire The brain: Integrated three-dimensional display of MR and PET images Radial. vol. 172 pp. 783-789 1989.
17. K. O. Lim and A. Pfefferbaum Segmentation of MR brain images into cerebrospinal fluid spaces white and gray matter J. Comput. Assist. Tomogr. vol. 13 no. 4 pp. 588-593 1989.
18. G. J. Harris E. H. Rhew T. Noga and G. D. Pearison User-friendly method for rapid brain and CSF volume calculation using transaxial MRI images Psych. Res.: Neuroimag. vol. 40 pp. 61-68 1991.
19. G. J. Harris P. E. Barta L. W. Peng S. Lee P. D. Brettschneider A. Shah J. D. Henderer T. E. Schlaepfer and G. D. Pearison MR volume segmentation of gray matter and white matter using manual thresholding: Dependence on image brightness Amer. J. Neuroradiol. vol. 15 pp. 225-230 1994.
20. S. C. Amartur D. Piraino and Y. Takefuji Optimization neural networks for the segmentation of magnetic resonance images IEEE Trans. Med. Imag. vol. 11 no. 2 pp. 215-220 1992.
21. M. Ozkan B. M. Dawant and R. J. Maciunas Neural-networkbased segmentation of multi-modal medical images: A comparative and prospective study IEEE Trans. Med. Imag. vol. 12 no. 3 pp. 534-544 1993.
22. M. E. Brandt T. P. Bohan L. A. Kramer and J. M. Fletcher Estimation of CSF white and gray matter volumes in hydrocéphalie children using fuzzy clustering of MR images Computerized Med. Imag. Graphics vol. 18 no. 1 pp. 25-34 1994.
23. L. O. Hall A. M. Bensaid L. P. Clarke R. P. Velthuizen M. S. Silbiger and J. C. Bezdek A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain IEEE Trans. Neural Networks vol. 3 no. 5 pp. 672-682 1992.
24. M. Jungke W. Von Seelen G. Bielke S. Meindl G. Krone M. Grigat P. Higer and P. Pfannenstiel Information processing in nuclear magnetic resonance imaging Magn. Reson. Imag. vol. 6 pp. 683-693 1988.
25. D. Y. Amamoto R. Kasturi and A. Mamourian Tissue-type discrimination in magnetic resonance images in Proc. 10th Int. Conf. Pattern Recogn. IEEE 1990 pp. 603-607.
26. M. O'Donnell J. C. Gore and W. J. Adams Toward an automated analysis system for nuclear magnetic resonance imaging-II: Initial segmentation algorithm Med. Phys. vol. 13 no. 3 pp. 293-297 1986.
27. D. A. Ortendahl N. M. Hylton L. Kaufman and L. E. Crooks Tissue characterization using intrinsic NMR parameters and a hierarchical processing algorithm IEEE Trans. Nuct. Sci. vol. NS-32 no. 1 pp. 875-879 1985.
28. G. Cohen N. C. Andreason R. Alliger S. Arndt J. Kuan W. T. C. Yuh and J. Ehrhardt Segmentation techniques for classification of brain tissue using magnetic resonance imaging Psych. Res.: Neuroimag. vol. 45 pp. 33-51 1992.
29. T. L. Jernigan G. A. Press and J. R. Hesselink Methods for measuring brain morphologic features on magnetic resonance images Arch. Neural. vol. 47 pp. 27-32 1990.
30. M. I. Kohn N. K. Tanna G. T. Herman S. M. Resnick P. D. Mozley R. E. Gur A. Alavi R. A. Zimmerman and R. C. Gur Analysis of brain and cerebrospinal fluid volumes with MR image-Part 1 : Methods reliability and validation Radial. vol. 178 pp. 115-122 1991.
31. L. P. Clarke R. P. Velthuizen S. Phuphanich J. D. Schellenberg J. A. Arrington and M. Silbiger MRI: Stability of three supervised segmentation techniques Magn. Reson. Imag. vol. 11 pp. 95-106 1993.
32. H. E. Cline W. E. Lorensen R. Kikinis and F. Jolesz Threedimensional segmentation of MR images of head using probability and connectivity J. Comput. Assist. Tomogr. vol. 14 no. 6 pp. 1037-1045 1990.
33. G. Gerig J. Martin R. Kikinis O. Kubler M. Shenton and F. A. Jolesz Unsupervised tissue type segmentation of 3-D dual-echo MR head data Image and Vision Computing vol. 10 no. 6 pp. 349-360 1992.
34. M. W. Vannier C. M. Speidel and D. L. Rickman Magnetic resonance imaging multispectral tissue classification News Physiolog. Sci. vol. 3 pp. 148-154 1988.
35. M. W. Vannier R. L. Butterfield D. Jordan W. A. Murphy R. G. Levitt and M. Gado Multispectral analysis of magnetic resonance images Radial vol. 154 pp. 221-224 1985.
36. Z. Liang R. J. Jaszczak and R. E. Coleman Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing IEEE Trans. Nucl. Sci. vol. 39 no. 4 pp. 1126-1133 1992.
37. Z. Liang Tissue classification and segmentation of MR images IEEE Eng. Med. Biol. Mag. pp. 81-85 1993.
38. Z. Liang J. R. MacFall and D. P. Harrington Parameter estimation and tissue segmentation from multispectral MR images IEEE Trans Med. Imag. vol. 13 no. 3 pp. 339-348 1994.
39. H. S. Choi D. R. Haynor and Y. Kim Partial volume tissue classification of multichannel magnetic resonance images-A mixed model IEEE Trans Med. Imag. vol. 10 no. 3 pp. 395-407 1991.
40. T. Taxt and A. Lundervold Multispectral analysis of the brain using magnetic resonance imaging IEEE Trans Med. Imag. vol. 13 no. 3 pp. 470-481 1994.
41. A. Lundervold and G. Storvik Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images IEEE Trans Med. Imag. vol. 14 no. 2 pp. 339-348 1995.
42. E. A. Ashton M. J. Berg K. J. Parker J. Weisberg C. W. Chen and L. Ketonen Segmentation and feature extraction techniques with applications to MRI head studies Magn. Reson. Med. vol. 33 pp. 670-677 1995.
43. S. P. Raya Low-level segmentation of 3-D magnetic resonance brain images-A rule based system IEEE Trans Med. Imag. vol. 9 no. 3 pp. 327-337 1990.
44. H. Suzuki and J. Toriwaki Automatic segmentation of head MRI images by knowledge guided thresholding Computerized Med. Imag. Graphics vol. 15 no. 4 pp. 233-240 1991.
45. D. N. Kennedy P. A. Filipek and V. S. Caviness Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging IEEE Trans Med. Imag. vol. 8 No. 1 pp. 1-7 1989.
46. G. Mittelhauber and F. Kruggel Fast segmentation of brain magnetic resonance tomograms in Conf. Comput. Vision Virtual Reality and Robotics in Medicine Apr. 1995 pp. 351-357.
47. W. C. Lin E. C. Tsao and C. T. Chen Constraint satisfaction neural networks for image segmentation Pattern Recogn. vol. 25 no. 7 pp. 679-693 1992.
48. P. Santago and H. D. Gage Quantification of MR brain images by mixture density and partial volume modeling IEEE Trans. Med. Imag. vol. 12 no. 3 pp. 566-574 1993.
49. C. DeCarli J. Maisog D. G. M. Murphy D. Teichberg S. I. Rapoport and B. Horwitz Method for quantification of brain ventricular and subarachnoid CSF volumes from MR images J. Comput. Assist. Tomogr. vol. 16 no. 2 pp. 274-284 1992.
50. D. L. Toulson and J. F. Boyce Segmentation of MR images using neural nets Image Vision Computing vol. 5 pp. 324-328 1992.
51. M. M. Chang A. M. Teklap and M. I. Sezan Bayesian segmentation of MR images using 3-D Gibbsian priors in Image Video Processing SPIE P roc. 1993 vol. 1903 pp. 122-133.
52. J. C. Bezdek L. O. Hall and L. P. Clarke Review of MR image segmentation techniques using pattern recognition Med. Phys. vol. 20 no. 4 pp. 1033-1048 1993.
53. L. P. Clarke R. P. Veltuizen M. A. Camacho J. J. Heine M. Vaidyanathan L. O. Hall R. W. Thatcher and M. L. Silbiger MRI segmentation: Methods and applications Magn. Reson. Imag. vol. 13 no. 3 pp. 343-368 1995.
54. M. Morrison and Y. Attikiouzel An introduction to the segmentation of magnetic resonance medical images Australian Comput. J. vol. 26 no. 3 pp. 90-98 1994.
55. G. Gerig O. Kubler R. Kikinis and F. A. Jolesz Nonlinear anisotropic filtering of MRI data IEEE Trans Med. Imag. vol. 11 no. 2 pp. 221-232 1992.
56. K. M. Ludeke P. Roschmann and R. Tischler Susceptibility artifacts in NMR imaging Magn. Reson. Imag. vol. 3 pp. 329-343 1985.
57. B. R. Condon J. Patterson D. Wyper A. Jenkins and D. M. Hardley Image nonuniformity in magnetic resonance imaging: Its magnitude and methods for its correction Br. J. Radial. vol. 60 pp. 83-87 1987.
58. D. A. G. Wicks G. J. Barker and P. S. Tofts Correction of intensity nonuniformity in MR images of any orientation Magn. Reson. Imag. vol. 11 pp. 183-196 1993.
59. B. M. Dawant A. P. Zijdenbos and R. A. Margolin Correction of intensity variations in MR images for computer-aided tissue classifica-tion IEEE Trans Med. Imag. vol. 12 no. 4 pp. 770-771 1993.
60. M. Tincher C. R. Meyer R. Gupta and D. M. Williams Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI IEEE Trans Med. Imag. vol. 12 no. 2 pp. 361-365 1993.
61. H. Chang and M. Fitzpatrick A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities IEEE Trans Med. Imag. vol. 11 no. 3 pp. 319-329 1992.
62. M. C. de Oliveira and R. I. Kitney Texture analysis for discrimination of tissues in MRI data in Proc. Computers in Cardiology IEEE 1991 pp. 481-484.
63. R. M. Haralick K. Shanmugam and I. Dinstein Textural features for image classification IEEE Trans. Syst. Man Cybern. vol. SMC-3 no. 6 pp. 610-621 1973.
64. H. A. Koenig R. Bachus and E. R. Reinhardt Pattern recognition for tissue characterization in magnetic resonance imaging Health Care Instrum. pp. 183-187 1986.
65. L. Kjaer C. Thomsen F. Gjerris B. Mosdal and O. Henriksen Tissue characterization of intracranial tumors by MRI imaging Acta Radiologica vol. 32 pp. 498-504 1989.
66. T. J. Hyman R. J. Kurland G. C. Levy and J. D. Shoop Characteriza-tion of normal brain tissue using seven calculated MRI parameters and a statistical analysis system Magn. Reson. Med. vol. 11 pp. 22-34 1989.
67. R. A. Redner and H. F. Walker Mixture densities maximum likelihood and the EM algorithm SIAM Rev. vol. 26 no. 2 pp. 195-239 1984.
68. D. M. Titterington A. F. M. Smith and U. E. Makov Statistical Analysis of Finite Mixture Distributions. New York: Wiley 1985.
69. J. Besag Spatial interaction and the statistical analysis of lattice systems J. Roy. Statist. Soc. B vol. 36 no. 2 pp. 192-225 1974.
70. M. Hassner and J. Sklansky The use of Markov random fields as models of texture Comput. Graphics Image Processing vol. 12 pp. 357-374 1980.
71. F. R. Hansen and H. Elliot Image segmentation using simple Markov field models Comput. Graphics Image Processing vol. 20 pp. 101-132 1982.
72. S. Geman and D. Geman Stochastic relaxation Gibbs distribution and the Bayesian restoration of images IEEE Trans. Pattern Anal. Machine Intell vol. PAMI-6 pp. 721-741 1984.
73. F. S. Cohen and D. Cooper Simple parallel hierarchical and relaxation algorithms for segmenting noncausal Markovian random fields IEEE Trans. Pattern Anal. Machine Intell. vol. PAMI-9 no. 2 pp. 195-219 1987.
74. R. C. Dubes and A. K. Jain Random field models in image analysis J. Appl. Statistics vol. 16 no. 2 pp. 131-164 1989.
75. B. S. Manjunath and R. Chelappa Unsupervised texture segmentation using Markov random field models IEEE Trans. Pattern Anal. Machine Intell. vol. 13 no. 5 pp. 478-482 1991.
76. R. Leahy T. Hebert and R. Lee Applications of Markov random fields in medical imaging Inform. Processing Med. Imag. pp. 1-14 1991.
77. R. O. Duda and P. E. Hart Pattern Classification and Scene Analysis. New York: Wiley 1973.
78. A. P. Dempster L. N. Laird and D. B. Rubin Maximum likelihood from incomplete data via the EM algorithm J. Roy. Statist. Soc. vol. 38-B pp. 1-38 1977.
79. N. Karssemeijer A statistical method for automatic labeling of tissues in medical images Machine Vision Applicat. vol. 3 pp. 75-86 1990.
80. J. Besag On the statistical analysis of dirty pictures J. Roy. Statist. Soc. B vol. 48 no. 3 pp. 259-302 1986.
81. J. Marroquin S. Mitter and T. Poggio Probabilistic solution of illposed problems in computational vision J. Amer. Statistical Assoc. vol. 82 no. 397 pp. 76-89 1987.
82. T. N. Pappas An adaptive clustering algorithm for image segmentation IEEE Trans. Signal Processing vol. 40 no. 4 pp. 901-914 1992.
83. H. Derin and H. Elliott Modeling and segmentation of noisy and textured images using Gibbs random fields IEEE Trans. Pattern Anal. Machine Intell. vol. PAMI-9 no. 1 pp. 39-55 1987.
84. S. Lakshmanan and H. Derin Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing IEEE Trans. Pattern Anal. Machine Intell. vol. 11 no. 8 pp. 799-813 1989.
85. S. Kirkpatrck C. D. Gelatt and M. P. Vecchi Optimization by simulated annealing Sci. vol. 220 No. 4598 pp. 671-680 1983.
86. S. L. Sclove Application of the conditional population mixer model to image segmentation IEEE Trans. Pattern Anal. Machine Intell. vol. PAMI-5 no. 4 pp. 428-433 1983.
87. M. R. Anderberg Cluster Analysis for Applications. New York: Academic 1973.
88. J. W. Snell M. Merickel J. Ortega J. Goble J. Brookeman and N. Kassell Boundary estimation of complex objects using hierarchical active surface templates Pattern Recogn. vol. 28 no. 10 pp. 1599-1609 1995.
89. J. N. Giedd J. W. Snell N. Lange J. C. Rajapakse P. Kozuch B. J. Casey D. Kaysen A. C. King S. D. Hamburger and J. L. Rapoport Quantitative magnetic resonance imaging of human brain development: Age 5-18 Cerebral Cortex vol. 6 no. 4 pp. 1-10 1996.
90. J. W. Bulte T. Douglas S. Mann R. B. Grankel B. M. Moskowitz R. A. Brooks C. D. Baumgarner J. Vymazal M. Strub and J. A. Frank Magnetoferritin: Characterization of a novel superparamagnetic MR contrast agent J. Magn. Reson. Imag. vol. 4 pp. 497-505 1994.
91. J. C. Rajapakse C. DeCarli A. Mclaughlin J. N. Giedd A. L. Krain S. D. Hamburger and J. L. Rapoport Cerebral magnetic resonance image segmentation using data fusion J. Comput. Assist. Tomogr. vol. 20 no. 2 pp. 206-218 1996.