Brain extraction based on locally linear representation-based classification

NeuroImage

Volumn 92, Issue , 2014, Pages 322-339

  Huang, Meiyan ^a   Yang, Wei ^a   Jiang, Jun ^a   Wu, Yao ^a   Zhang, Yu ^a   Chen, Wufan ^a   Feng, Qianjin ^a  

^a SOUTHERN MEDICAL UNIVERSITY   (China)

Author keywords

Brain extraction; Label fusion; Local anchor embedding; Locally Linear Representation based Classification

Indexed keywords

AGED; ALZHEIMER DISEASE; ARTICLE; BRAIN EXTRACTION; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA BASE; DATA PROCESSING; FEMALE; HUMAN; IMAGE ANALYSIS; INTERMETHOD COMPARISON; LOCALLY LINEAR REPRESENTATION BASED CLASSIFICATION; MAJOR CLINICAL STUDY; MALE; MEASUREMENT ACCURACY; MILD COGNITIVE IMPAIRMENT; NEUROIMAGING; PRIORITY JOURNAL; ADOLESCENT; ADULT; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BRAIN; CHILD; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SKULL; STATISTICAL MODEL; VERY ELDERLY; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; ALZHEIMER DISEASE; BRAIN; CHILD; COMPUTER SIMULATION; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SKULL; SUBTRACTION TECHNIQUE; YOUNG ADULT;

EID: 84896005008     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.01.059     Document Type: Article

References (38)

1. Ashburner J., Friston K.J. Voxel-based morphometry-the methods. NeuroImage 2000, 11:805-821.
2. Baillard C., Hellier P., Barillot C. Segmentation of brain 3D MR images using level sets and dense registration. Med. Image Anal. 2001, 5:185-194.
3. Buades A., Coll B., Morel J.M. A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 2005, 4:490-530.
4. Carass A., Cuzzocreo J., Wheeler M.B., Bazin P.L., Resnick S.M., Prince J.L. Simple paradigm for extra-cerebral tissue removal: algorithm and analysis. NeuroImage 2011, 56:1982-1992.
5. Chiverton J., Wells K., Lewis E., Chen C., Podda B., Johnson D. Statistical morphological skull stripping of adult and infant MRI data. Comput. Biol. Med. 2007, 37:342-357.
6. Dale A.M., Fischl B., Sereno M.I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 1999, 9:179-194.
7. Eskildsen S.F., Coupe P., Fonov V., Manjon J.V., Leung K.K., Guizard N., Wassef S.N., Ostergaard L.R., Collins D.L., Alzheimer's Disease Neuroimaging I. BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 2012, 59:2362-2373.
8. Gao Y., Liao S., Shen D. Prostate segmentation by sparse representation based classification. Med. Phys. 2012, 39:6372-6387.
9. Gong D., Sha F., Medioni G. Locally linear denoising on image manifolds. 13th International Conference on Artifical Intelligence and Statistics 2010.
10. Hahn H.K., Peitgen H.-O. The skull stripping problem in MRI solved by a single 3D watershed transform. Med. Image Comput. Comput. Assist. Interv. 2000, 134-143.
11. Iglesias J.E., Liu C.Y., Thompson P.M., Tu Z.W. Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans. Med. Imaging 2011, 30:1617-1634.
12. Khan A.R., Cherbuin N., Wen W., Anstey K.J., Sachdev P., Beg M.F. Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): validation on hippocampus segmentation. NeuroImage 2011, 56:126-139.
13. Lemieux L., Hagemann G., Krakow K., Woermann F.G. Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data. Magn. Reson. Med. 1999, 42:127-135.
14. Leung K.K., Barnes J., Modat M., Ridgway G.R., Bartlett J.W., Fox N.C., Ourselin S. Brain MAPS: an automated, accurate and robust brain extraction technique using a template library. NeuroImage 2011, 55:1091-1108.
15. Liu W., He J.F., Chang S.-F. Large graph construction for scalable semi-supervised learning. 27th International Conference on Machine Learning 2010.
16. Macqueen J. Some methods for classification and analysis of multivariate observations. Fifth Berkeley Symp. on Math. Statist. and Prob 1967, 281-297.
17. Mazziotta J.C., Toga A.W., Evans A., Fox P., Lancaster J. A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). NeuroImage 1995, 2:89-101.
18. Mazziotta J., Toga A., Evans A., Fox P., Lancaster J., Zilles K., Woods R., Paus T., Simpson G., Pike B., Holmes C., Collins L., Thompson P., MacDonald D., Iacoboni M., Schormann T., Amunts K., Palomero-Gallagher N., Geyer S., Parsons L., Narr K., Kabani N., Le Goualher G., Boomsma D., Cannon T., Kawashima R., Mazoyer B. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 2001, 356:1293-1322.
19. Mikheev A., Nevsky G., Govindan S., Grossman R., Rusinek H. Fully automatic segmentation of the brain from T1-weighted MRI using bridge burner algorithm. J. Magn. Reson. Imaging 2008, 27:1235-1241.
20. Mueller S.G., Weiner M.W., Thal L.J., Petersen R.C., Jack C., Jagust W., Trojanowski J.Q., Toga A.W., Beckett L. The Alzheimer's disease neuroimaging initiative. Neuroimaging Clin. N. Am. 2005, 15:869-877. (xi-xii).
21. Nyul L.G., Udupa J.K., Zhang X. New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 2000, 19:143-150.
22. Park J.G., Lee C. Skull stripping based on region growing for magnetic resonance brain images. NeuroImage 2009, 47:1394-1407.
23. Rehm K., Schaper K., Anderson J., Woods R., Stoltzner S., Rottenberg D. Putting our heads together: a consensus approach to brain/non-brain segmentation in T1-weighted MR volumes. NeuroImage 2004, 22:1262-1270.
24. Rex D.E., Shattuck D.W., Woods R.P., Narr K.L., Luders E., Rehm K., Stoltzner S.E., Rottenberg D.A., Toga A.W. A meta-algorithm for brain extraction in MRI. NeuroImage 2004, 23:625-637.
25. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
26. Sadananthan S.A., Zheng W.L., Chee M.W.L., Zagorodnov V. Skull stripping using graph cuts. NeuroImage 2010, 49:225-239.
27. Segonne F., Dale A.M., Busa E., Glessner M., Salat D., Hahn H.K., Fischl B. A hybrid approach to the skull stripping problem in MRI. NeuroImage 2004, 22:1060-1075.
28. Shan Z.Y., Yue G.H., Liu J.Z. Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. NeuroImage 2002, 17:1587-1598.
29. Shattuck D.W., Sandor-Leahy S.R., Schaper K.A., Rottenberg D.A., Leahy R.M. Magnetic resonance image tissue classification using a partial volume model. NeuroImage 2001, 13:856-876.
30. Shen D., Davatzikos C. Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warping. NeuroImage 2004, 21:1508-1517.
31. Shi F., Wang L., Dai Y.K., Gilmore J.H., Lin W.L., Shen D.G. LABEL: pediatric brain extraction using learning-based meta-algorithm. NeuroImage 2012, 62:1975-1986.
32. Sled J.G., Zijdenbos A.P., Evans A.C. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 1998, 17:87-97.
33. Smith S.M. Fast robust automated brain extraction. Hum. Brain Mapp. 2002, 17:143-155.
34. van der Kouwe A.J., Benner T., Salat D.H., Fischl B. Brain morphometry with multiecho MPRAGE. NeuroImage 2008, 40:559-569.
35. Wang J.J., Yang J.C., Yu K., Lv F.J., Huang T., Gong Y.H. Locality-constrained linear coding for image classification. Comput. Vis. Pattern Recognit. 2010, 3360-3367.
36. Ward B.D. 3d Intracranial: Automatic Segmentation of Intracranial Region 1999.
37. Wright J., Yang A.Y., Ganesh A., Sastry S.S., Ma Y. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31:210-227.
38. Zhuang A.H., Valentino D.J., Toga A.W. Skull-stripping magnetic resonance brain images using a model-based level set. NeuroImage 2006, 32:79-92.