Robust MRI brain tissue parameter estimation by multistage outlier rejection

Magnetic Resonance in Medicine

Volumn 59, Issue 4, 2008, Pages 866-873

  Manjón, José V ^a   Tohka, Jussi ^b   García Martí, Gracian ^a   Carbonell Caballero, José ^a   Lull, Juan J ^a   Martí Bonmatí, Luís ^c,d   Robles, Montserrat ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^b TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^c Quiron Hospital Group   (Spain)

^d HOSPITAL UNIVERSITARIO DR PESET   (Spain)

Author keywords

Partial volume; Robust estimation; Segmentation

Indexed keywords

BRAIN; IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; PARAMETER ESTIMATION; STATISTICS; TISSUE;

BRAIN TISSUE CLASSIFICATIONS; BRAIN TISSUE PARAMETERS; INTENSITY DISTRIBUTION; PARTIAL VOLUME EFFECT; PARTIAL VOLUMES; ROBUST ESTIMATION; STATE-OF-THE-ART METHODS; TISSUE CHARACTERISTICS;

TISSUE ENGINEERING;

ARTICLE; BRAIN SIZE; BRAIN TISSUE; COMPARATIVE STUDY; HUMAN; HUMAN EXPERIMENT; IMAGE QUALITY; NEUROIMAGING; NORMAL DISTRIBUTION; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE; NUCLEAR MAGNETIC RESONANCE IMAGING; QUALITATIVE ANALYSIS;

EID: 41849121906     PISSN: 07403194     EISSN: 15222594     Source Type: Journal    
DOI: 10.1002/mrm.21521     Document Type: Article

References (29)

1. Gonzalez Ballester MA, Zisserman A, Brady M. Segmentation and measurement of brain structures in MRI including confidence bounds. Med Image Anal 2000;4:189-200.
2. Santago P, Gage HD. Quantification of MR images by mixture density and partial volume modelling. IEEE Trans Med Imaging 1993;12:566-574.
3. Laidlaw D, Flescher K, Barr A. Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Trans Med Imaging 1998;17:74-86.
4. Ruan S, Jaggi C, Xue J, Fadili J, Bloyet D. Brain tissue classification of magnetic resonance images using partial volume modelling. IEEE Trans Med Imaging 2000;19:1179-1187.
5. Grawbowski T, Frank R, Szumski N, Brown C, Damasio H. Validation of partial tissue segmentation of single-channel magnetic resonance images of the brain. Neuroimage 2000;12:640-656.
6. Shattuck D, Sandor-Leahy S, Schaper K, Rottenberg D, Leahy R. Magnetic resonance image tissue classification using a partial volume model. Neuroimage 2001;13:856-876.
7. Pham D, Prince J. Adaptative fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imaging 1999;18:737-752.
8. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans Med Imaging 2001;20:45-57.
9. Noe A, Gee JC. Partial volume segmentation of cerebral MRI scans with mixture model clustering. Inf Process Med Imaging 2001:423-430.
10. Van Leemput KF, Maes D, Vandermeulen P, Suetens A. Unifying framework for partial volume segmentation of brain MR images. IEEE Trans Med Imaging 2003;22:105-119.
11. Williamson D, Thacker N, Williams S, Pokric M. Partial volume tissue segmentation using grey level gradient. MIUA 2002:17-20.
12. Tohka J, Krestyannikov E, Dinov I, MacKenzie-Graham A, Shattuck D, Ruotsalainen U, Toga A. Genetic algorithms for finite mixture model based voxel classification in neuroimaging. IEEE Trans Med Imaging 2007;26:696-711
13. Wang D, Doddrell DM. A segmentation-based and partial-volume-compensated method for an accurate measurement of lateral ventricular volumes on T(1)-weighted magnetic resonance images. Magn Reson Imaging 2001;19:267-273.
14. Tohka J, Zijdenbos A, Evans A. Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 2004;23:84-97.
15. Rousseeuw PJ, Leroy AM. Robust regression and outlier detection. New York: Wiley-Interscience; 1987.
16. Belaroussi B, Milles J, Carme S, Min Zhu Y, Benoit H. Intensity non-uniformity correction in MRI: existing methods and their validation. Med Image Anal 2005;22:121-132.
17. Sijbers J. Signal and noise estimation from magnetic resonance images. Doctoral thesis, University of Antwerp; 1998.
18. Prima S, Ayache N, Barrick T, Roberts N. Maximum likelihood estimation of the bias field in MR brain images: investigating different modelings of the imaging process. MICCAI 2001:811-819.
19. MacQueen JB. Some methods for classification and analysis of multivariate observations. In: Proc 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press; 1967;1:281-297
20. Dempster A, Laird N, Rubing D. Maximum likelihood from imcomplete data via EM algorithm. J R Soc 1977;39:1-38.
21. Cocosco CA, Kollokian V, Kwan RK-S, Evans AC. BrainWeb: online interface to a 3D MRI simulated brain database. Neuroimage 5 (4), part 2/4, S425, 1997. In: Proc 3rd International Conference on Functional Mapping of the Human Brain, Copenhagen; 1997.
22. Wells W III, Crimson W, Kikinis R, Jolesz F. Adaptative segmentation of MRI data. IEEE Trans Med Imaging 1996;15:429-442.
23. Manjón JV, Lull JJ, Carbonell-Caballero J, García- Martí G, Martí-Bonmatí L, Robles M. A nonparametric MRI inhomogeneity correction method. Med Image Anal 2007;11:336-345.
24. Ashburner J, Friston KJ. Voxel-based morphometry - the methods. Neuroimage 2000;11:805-821.
25. Fischl B, Salat DH, van der Kouwe AJW, Makris N, Segonne F, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23(suppl 1):S69-84
26. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC. Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 1998;17:463-468.
27. Kwan RK-S, Evans AC, Pike GB. MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans Med Imaging 1999;18:1085-1097.
28. Choi HS, Haynor DR, Kim Y. Partial volume tissue classification of multichannel magnetic resonance images - a mixel model. IEEE Trans Med Imag 1991;10:395-407.
29. Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med 1995;34:910-914.