Non-locally regularized segmentation of multiple sclerosis lesion from multi-channel MRI data

Magnetic Resonance Imaging

Volumn 32, Issue 8, 2014, Pages 1058-1066

  Gao, Jingjing ^a,b   Li, Chunming ^b,e   Feng, Chaolu ^c   Xie, Mei ^a   Yin, Yilong ^d   Davatzikos, Christos ^b,e  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b University of PA   (United States)

^c NORTHEASTERN UNIVERSITY   (China)

^d SHANDONG UNIVERSITY   (China)

^e UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Bias field estimation; Energy minimization; Lesion segmentation; Multi channel MR images; Nonlocal means

Indexed keywords

ALGORITHM; ARTICLE; BRAIN REGION; BRAIN SEGMENTATION; CALCULATION; CONTROLLED STUDY; FUZZY SYSTEM; MATHEMATICAL COMPUTING; MEASUREMENT ACCURACY; MULTIPLE SCLEROSIS; NEUROIMAGING; NEUROLOGICAL AND SENSORIAL PROCEDURES; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROCESS OPTIMIZATION; QUANTITATIVE ANALYSIS; SCORING SYSTEM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; AUTOMATION; BRAIN; FUZZY LOGIC; HUMAN; IMAGE PROCESSING; LABORATORY DIAGNOSIS; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; AUTOMATION; BRAIN; FALSE POSITIVE REACTIONS; FUZZY LOGIC; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; MULTIPLE SCLEROSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84906064600     PISSN: 0730725X     EISSN: 18735894     Source Type: Journal    
DOI: 10.1016/j.mri.2014.03.006     Document Type: Article

References (38)

1. Li C., Gatenby C., Wang L., Gore J.C. A robust parametric method for bias field estimation and segmentation of mr images. CVPR 2009. IEEE Conference on, IEEE 2009, 218-223.
2. Schmahmann J.D., Smith E.E., Eichler F.S., Filley C.M. Cerebral white matter. Ann N Y Acad Sci 2008, 1142(1):266-309.
3. Prins N.D., van Straaten E.C., van Dijk E.J. 3d segmentation in the clinic: a grand challenge ii: Ms lesion segmentation. MIDAS J 2004, 62(9):1522-1539.
4. Wahlund L.O., Barkhof F., Fazekas F. A new rating scale for age-related white matter changes applicable to mri and ct. Stroke 2001, 32:1318-1322.
5. Scheltens P., Barkhof F., Leys D. A semiquantative rating scale for the assessment of signalhyperintensities on magneticresonanceimaging. J Neurol Sci 1993, 114(1):7-12.
6. D. Garcla-Lorenzo, S. Francis, S. Narayanan, D. L. Arnold, D. Louis Collins, Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging, Medical image analysis, 2013.
7. Shiee N., Bazin P.-L. A semiquantative rating scale for the assessment of signalhyperintensities on magneticresonanceimaging. NeuroImage 2010, 49(1):1524C-1535C.
8. Hirono N. Impact of white matter changes on clinical manifestation of Alzheimer's disease: a quantitative study. Stroke 2000, 31:2182-2188.
9. Jr C. Flair histogram segmentation for measurement of leukoaraiosis volume. Magn Reson Imaging 2001, 14:668-676.
10. Klöppel S., Abdulkadir A., Hadjidemetriou S., Issleib S., Frings L., Thanh T.N., et al. A comparison of different automated methods for the detection of white matter lesions in mri data. NeuroImage 2011, 57(2):416-422.
11. Llado X., Oliver A., Cabezas M., Freixenet J., Vilanova J.C., Quiles A., et al. Segmentation of multiple sclerosis lesions in brain mri: a review of automated approaches. Inf Sci 2012, 186(1):164-185.
12. Yamamoto D., Arimura H., Kakeda S. Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: false positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput Med Imaging Graph 2010, 34(5):404C-413C.
13. Breiman L. Random forests. Mach Learn 2001, 45(1):5-32.
14. Lao Z., Shen D. Computer-assistedsegmentation of white matter lesions in 3d mr images using support vector machine. Acad Radiol 2008, 15(3):300C-313C.
15. Quddus A., Fieguth P. Adaboost and support vector machines for white matter lesion segmentation in mr images. Shanghai 2005, 1:463-466.
16. Anbeek P., Vincken K.L. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 2004, 8(3):205C-215C.
17. Anbeek P., Vincken K.L. Probabilistic segmentation of white matter lesions in mr imaging. NeuroImage 2004, 21(3):1318C-1322C.
18. Wu Y., Warfield S.K. Automated segmentation of multiple sclerosis lesion subtypes with multichannel mri. NeuroImage 2006, 32(3):1205C-1215C.
19. Warfield S., Kaus M. Adaptive, template moderated, spatially varying statistical classification. Med Image Anal 2000, 4(3):677C-688C.
20. Leemput K.V., Maes F., Vandermeulen D., Colchester A. Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans Med Imaging 2001, 20(8):677C-688C.
21. Ait-Ali L., Prima S., Hellier P., Carsin B., Edan G.G., Barillot C. Strem: a robust multidimensional parametric method to segment ms lesions in mri. MICCAI'05 2005, 409-416.
22. García-Lorenzo D., Prima S., Collins D. Combining robust expectation maximization and mean shift algorithms for multiple sclerosis brain segmentation. MCCAI'08 2008, 82-91.
23. Bricq S., Collet C., Armspach J.P. Lesion detection in 3d brain mri using trimmed likelihood estimator and probabilistic atlas. ISBI'08 2008, 93-96.
24. Freifeld O., Greenspan H., Goldberger J. Lesion detection in noisy mr brain images using constrained gmm and active contours. ISBI'07 2007, 596-599.
25. L. Shi, D. Wang, S. Liu, Y. Pu, Y. Wang, W. C. Chu, A. T. Ahuja, Y. Wang, Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction, Journal of neuroscience methods, 2013.
26. Harmouche R., Collins L., Arnold D., Francis S., Arbel T. Bayesian ms lesionclas- sification modeling regional and local spatial information. ICPR'06 2006, 984-987.
27. Khademi A., Venetsanopoulos A., Moody A.R. Robust white matter lesion segmentation in flair mri, biomedical engineering. IEEE Trans 2012, 59(3):860-871.
28. Wallace C.J., Seland T.P., Fong T.C. Multiple sclerosis: the impact of mr imaging. Am J Roentgenol 1992, 158(1):849-857.
29. Wiebe S., Lee D.H., Karlik S.J., Hopkins M., Vandervoort M.K. Serial cranial and spinal cord magnetic resonance imaging in multiple sclerosis. Ann Neurol 1992, 32:643-650.
30. Truyen L. Magnetic resonance imaging in multiple sclerosis: a review. Acta Neurol Belg 1994, 94(1):98-102.
31. Fazekas F., Barkhof F., Filippi M., Grossman R.I., Li D.K. The contribution of magnetic resonance imaging to the diagnosis of multiple sclerosis. Neurology 1999, 53(1):448-456.
32. Pham D.L., Prince J.L. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans Med Imaging 1999, 18(9):737-752.
33. Buades A., Coll B., Morel J.-M. Nonlocal image and moive denoising. Int J Comput Vis 2008, 76:123-139.
34. Styner M.W., Niessen S., Walsum W.V., Metz T., Schaap C., Deng M., et al. Ms lesion segmentation, challenge 2008.
35. Van Leemput K., Maes F., Bello F., Vandermeulen D., Colchester A., Suetens P. Automated segmentation of ms lesions from multichannel mr images. Medical Image Computing and Computer-Assisted Intervention-MICCAI99 1999, 11-21. Springer.
36. Collins D.L., Zijdenbos A.P., Kollokian V.V. Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 1999, 17(3):463-468.
37. Kwan R.K.-S., Evans A.C., Pike G.B. An extensible mri simulator for post-processing evaluation. Lect Notes Comput Sci 1996, 1131:135-140.
38. Richard Sims V.G. Aurelie Isambert, A pre-clinical assessment of an atlas- based automatic segmentation tool for the head and neck. Radiother Oncol 2009, 93:474-478.