216 related articles for article (PubMed ID: 22055751)
1. An anisotropic images segmentation and bias correction method.
Chen Y; Zhang J; Yang J
Magn Reson Imaging; 2012 Jan; 30(1):85-95. PubMed ID: 22055751
[TBL] [Abstract][Full Text] [Related]
2. Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.
Chen Y; Zhao B; Zhang J; Zheng Y
Magn Reson Imaging; 2014 Sep; 32(7):941-55. PubMed ID: 24832358
[TBL] [Abstract][Full Text] [Related]
3. An improved variational level set method for MR image segmentation and bias field correction.
Zhan T; Zhang J; Xiao L; Chen Y; Wei Z
Magn Reson Imaging; 2013 Apr; 31(3):439-47. PubMed ID: 23219273
[TBL] [Abstract][Full Text] [Related]
4. Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains.
Bricq S; Collet Ch; Armspach JP
Med Image Anal; 2008 Dec; 12(6):639-52. PubMed ID: 18440268
[TBL] [Abstract][Full Text] [Related]
5. Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization.
Salvado O; Hillenbrand C; Zhang S; Wilson DL
IEEE Trans Med Imaging; 2006 May; 25(5):539-52. PubMed ID: 16689259
[TBL] [Abstract][Full Text] [Related]
6. Correction of bias field in MR images using singularity function analysis.
Luo J; Zhu Y; Clarysse P; Magnin I
IEEE Trans Med Imaging; 2005 Aug; 24(8):1067-85. PubMed ID: 16092338
[TBL] [Abstract][Full Text] [Related]
7. A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image.
Ji ZX; Sun QS; Xia DS
Comput Med Imaging Graph; 2011 Jul; 35(5):383-97. PubMed ID: 21256710
[TBL] [Abstract][Full Text] [Related]
8. Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.
Zhou Y; Bai J
IEEE Trans Biomed Eng; 2007 Jan; 54(1):122-9. PubMed ID: 17260863
[TBL] [Abstract][Full Text] [Related]
9. Interplay between intensity standardization and inhomogeneity correction in MR image processing.
Madabhushi A; Udupa JK
IEEE Trans Med Imaging; 2005 May; 24(5):561-76. PubMed ID: 15889544
[TBL] [Abstract][Full Text] [Related]
10. An improved level set method for brain MR images segmentation and bias correction.
Chen Y; Zhang J; Macione J
Comput Med Imaging Graph; 2009 Oct; 33(7):510-9. PubMed ID: 19481420
[TBL] [Abstract][Full Text] [Related]
11. Evaluating intensity normalization on MRIs of human brain with multiple sclerosis.
Shah M; Xiao Y; Subbanna N; Francis S; Arnold DL; Collins DL; Arbel T
Med Image Anal; 2011 Apr; 15(2):267-82. PubMed ID: 21233004
[TBL] [Abstract][Full Text] [Related]
12. Topology-preserving tissue classification of magnetic resonance brain images.
Bazin PL; Pham DL
IEEE Trans Med Imaging; 2007 Apr; 26(4):487-96. PubMed ID: 17427736
[TBL] [Abstract][Full Text] [Related]
13. An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images.
Ivanovska T; Laqua R; Wang L; Schenk A; Yoon JH; Hegenscheid K; Völzke H; Liebscher V
Comput Med Imaging Graph; 2016 Mar; 48():9-20. PubMed ID: 26741125
[TBL] [Abstract][Full Text] [Related]
14. A novel content-based active contour model for brain tumor segmentation.
Sachdeva J; Kumar V; Gupta I; Khandelwal N; Ahuja CK
Magn Reson Imaging; 2012 Jun; 30(5):694-715. PubMed ID: 22459443
[TBL] [Abstract][Full Text] [Related]
15. A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: with applications to DTI-tract extraction.
Awate SP; Zhang H; Gee JC
IEEE Trans Med Imaging; 2007 Nov; 26(11):1525-36. PubMed ID: 18041267
[TBL] [Abstract][Full Text] [Related]
16. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.
Cuadra MB; Cammoun L; Butz T; Cuisenaire O; Thiran JP
IEEE Trans Med Imaging; 2005 Dec; 24(12):1548-65. PubMed ID: 16350916
[TBL] [Abstract][Full Text] [Related]
17. A fully automated algorithm under modified FCM framework for improved brain MR image segmentation.
Sikka K; Sinha N; Singh PK; Mishra AK
Magn Reson Imaging; 2009 Sep; 27(7):994-1004. PubMed ID: 19395212
[TBL] [Abstract][Full Text] [Related]
18. Complex threshold method for identifying pixels that contain predominantly noise in magnetic resonance images.
Pandian DS; Ciulla C; Haacke EM; Jiang J; Ayaz M
J Magn Reson Imaging; 2008 Sep; 28(3):727-35. PubMed ID: 18777533
[TBL] [Abstract][Full Text] [Related]
19. Feature-preserving MRI denoising: a nonparametric empirical Bayes approach.
Awate SP; Whitaker RT
IEEE Trans Med Imaging; 2007 Sep; 26(9):1242-55. PubMed ID: 17896596
[TBL] [Abstract][Full Text] [Related]
20. Automatic estimation of the noise variance from the histogram of a magnetic resonance image.
Sijbers J; Poot D; den Dekker AJ; Pintjens W
Phys Med Biol; 2007 Mar; 52(5):1335-48. PubMed ID: 17301458
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
