Improved estimates of partial volume coefficients from noisy brain MRI using spatial context

NeuroImage

Volumn 53, Issue 2, 2010, Pages 480-490

  Manjón, José V ^a   Tohka, Jussi ^b   Robles, Montserrat ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^b TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Markov Random Fields; MRI; Non Local Filtering; Partial volume; Segmentation

Indexed keywords

ARTICLE; BRAIN SIZE; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE QUALITY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; BRAIN; COMPUTER SIMULATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MODELS, STATISTICAL; PHANTOMS, IMAGING; SOFTWARE;

EID: 77956060083     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2010.06.046     Document Type: Article

References (42)

1. Anbeek P., Vincken K.L., van Bochove G.S., van Osch M., van der Grond J. Probabilistic segmentation of brain tissue in MR imaging. Neuroimage 2005, 27:795-804.
2. Ashburner J., Friston K.J. Unified segmentation. Neuroimage 2005, 26:839-851.
3. Aubert-Broche B., Griffin M., Pike G.B., Evans A.C., Collins D.L. Twenty new digital brain phantoms for creation of validation image data bases. IEEE T. Med. Imaging 2006, 25(11):1410-1416.
4. 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. 2006, 10:121-132.
5. Besag J. On the statistical analysis of dirty pictures. J. R. Stat. Soc, Series B 1986, 48:259-302.
6. Bricq S., Collet Ch., Armspach J.P. Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains. Med. Image Anal. 2008, 12(6):639-652.
7. Brouwer R.M., Hulshoff Pol H.E., Schnack H.G. Segmentation of MRI brain scans using non-uniform partial volume densities. Neuroimage 2010, 49:467-477.
8. Buades A., Coll B., Morel J.M. A non local algorithm for image denoising. IEEE Int. Conf. Compu. Vis. Pattern Recognition. 2005, 2:60-65.
9. Choi H.S., Haynor D.R., Kim Y.M. Partial volume tissue classification of multichannel magnetic resonance images-a mixel model. IEEE T. Med. Imaging 1991, 10:395-407.
10. Cocosco C.A., Zijdenbos A.P., Evans A.C. A fully automatic and robust brain MRI tissue classification method. Med. Image Anal. 2003, 7(4):513-527.
11. Collins D.L., Zijdenbos A.P., Kollokian V., Sled J.G., Kabani N.J., Holmes C.J., Evans A.C. Design and construction of a realistic digital brain phantom. IEEE T. Med. Imaging 1998, 17(3):463-468.
12. Coupé P., Yger P., Prima S., Hellier P., Kervrann C., Barillot C. An optimized blockwise non local means denoising filter for 3D magnetic resonance images. IEEE T. Med. Imaging 2008, 27(4):425-441.
13. Duda R.O., Hart P.E., Stork D.G. Pattern Classification, Wiley-Interscience; 2 edition 2000.
14. Garcia-Marti G., Aguilar E.J., Lull J.J., Marti-Bonmati L., Escarti M.J., Manjon J.V., Robles M., San Juan J. Schizophrenia with auditory hallucinations: a voxel-based morphometry study. Prog. Neuropsychopharmacol. Biol. Psychiatry 2008, 32:72-80.
15. Gerig G., Kubler O., Kikinis R., Jolesz F.A. Nonlinear anisotropic filtering of MRI data. IEEE T. Med. Imaging 1992, 11:221-232.
16. Gonzalez-Ballester M.A., Zisserman A., Brady M. Segmentation and measurement of brain structures in MRI including confidence bounds. Med. Image Anal. 2000, 4(3):189-200.
17. 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.
18. Kent J.T., Mardia K.V. Spatial classification using fuzzy membership models. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 10(5):659-671.
19. Kim J.S., Singh V., Lee J.K., Lerch J., Ad-Dab'bagh Y., MacDonald D., Lee J.M., Kim S.I., Evans A.C. Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 2005, 27:210-221.
20. Kwan R.K.-S., Evans A.C., Pike G.B. MRI simulation-based evaluation of image-processing and classification methods. IEEE T. Med. Imaging 1999, 18(11):1085-1097.
21. Laidlaw D., Flescher K., Barr A. Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE T. Med. Imaging 1998, 17:74-86.
22. Li Z. Markov random fields in image analysis 2009, Springer. 3rd.
23. Li X., Li L., Lu H., Liang Z. Partial volume segmentation of brain magnetic resonance images based on maximum a posteriori probability. Med. Phys. 2005, 32(7):2337-2345.
24. Manjón J.V., Lull J.J., Carbonell-Caballero J., García-Martí G., Martí-Bonmatí L., Robles M. A nonparametric MRI inhomogeneity correction method. Med. Image Anal. 2007, 11:336-345.
25. Manjón J.V., Tohka J., García-Martí G., Carbonell-Caballero J., Lull J.J., Martí-Bonmatí L., Robles M. Robust MRI Brain Tissue Parameter Estimation by Multistage Outlier Rejection. Magn. Reson. Med. 2008, 59:866-873.
26. Mietchen D., Gaser C. Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution. Front. Neuroinform. 2009, 3:25.
27. Pham D.L. Spatial models for fuzzy clustering. Comput. Vis. Image Underst. 2001, 84(2):285-297.
28. Pham D.L., Bazin P.L. Unspervised tissue classification. Handbook of Medical Processing and Analysis 2009, 209-222. Second edition. I.N. Bankman (Ed.).
29. Pham D.L., Prince J. Partial volume estimation and the fuzzy c-means algorithm. Proc.-Int. Conf. Image Proc. (ICIP98) 1998, 3:819-822.
30. Pizurica A., Philips W., Lemahieu I., Acheroy M. A versatile wavelet domain noise filtration technique for medical imaging. IEEE T. Med. Imaging 2003, 22(3):323-331.
31. Prima S., Ayache N., Barrick T., Roberts N. Maximum likelihood estimation of the bias field in MR brain images: investigating different modellings of the imaging process. MICCAI 2001, 2001:811-819.
32. Ruan S., Jaggi C., Xue J., Fadili J., Bloyet D. Brain tissue classification of magnetic resonance images using partial volume modelling. IEEE T. Med. Imaging 2000, 19:1179-1187.
33. Ruan S., Moretti B., Fadili J., Bloyet D. Fuzzy Markovian segmentation in application of magnetic resonance images. Comput. Vis. Image Underst. 2002, 85:54-69.
34. Santago P., Gage H.D. Quantification of MR images by mixture density and partial volume modelling. IEEE T. Med. Imaging 1993, 12:566-574.
35. Shattuck D., Sandor-Leahy S., Schaper K., Rottenberg D., Leahy R. Magnetic resonance image tissue classification using a partial volume model. Neuroimage 2001, 13(5):856-876.
36. Sijbers J. Signal and noise estimation from magnetic resonance images 1998, Doctoral thesis, Antwerpen.
37. Sled J.G., Zijdenbos A.P., Evans A.C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE T. Med. Imaging 1998, 17:87-97.
38. Tohka J., Zijdenbos A., Evans A. A Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 2004, 23:84-97.
39. 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 T. Med. Imaging 2007, 26(5):696-711.
40. Tohka J., Dinov I.D., Shattuck D.W., Toga A.W. Brain MRI tissue classification based on local Markov random fields. Magn. Reson. Imaging 2010, 28(4):557-573.
41. Tomasi C., Manduchi R. Bilateral filtering for gray and color images. Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India 1998.
42. Van Leemput K.F., Maes D.Vandermeulen, Suetens P. A unifying framework for partial volume segmentation of brain MR images. IEEE T. Med. Imaging 2003, 22(1):105-119.