444 related articles for article (PubMed ID: 33130495)
1. Fast level set method for glioma brain tumor segmentation based on Superpixel fuzzy clustering and lattice Boltzmann method.
Khosravanian A; Rahmanimanesh M; Keshavarzi P; Mozaffari S
Comput Methods Programs Biomed; 2021 Jan; 198():105809. PubMed ID: 33130495
[TBL] [Abstract][Full Text] [Related]
2. A level set method based on domain transformation and bias correction for MRI brain tumor segmentation.
Khosravanian A; Rahmanimanesh M; Keshavarzi P; Mozaffari S
J Neurosci Methods; 2021 Mar; 352():109091. PubMed ID: 33515604
[TBL] [Abstract][Full Text] [Related]
3. Automatic glioma segmentation based on adaptive superpixel.
Wu Y; Zhao Z; Wu W; Lin Y; Wang M
BMC Med Imaging; 2019 Aug; 19(1):73. PubMed ID: 31443642
[TBL] [Abstract][Full Text] [Related]
4. Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.
Şişik F; Sert E
Med Hypotheses; 2020 Mar; 136():109507. PubMed ID: 31812927
[TBL] [Abstract][Full Text] [Related]
5. Brain tissue segmentation using improved kernelized rough-fuzzy C-means with spatio-contextual information from MRI.
Halder A; Talukdar NA
Magn Reson Imaging; 2019 Oct; 62():129-151. PubMed ID: 31247252
[TBL] [Abstract][Full Text] [Related]
6. A fast and robust level set method for image segmentation using fuzzy clustering and lattice Boltzmann method.
Balla-Arabé S; Gao X; Wang B
IEEE Trans Cybern; 2013 Jun; 43(3):910-20. PubMed ID: 23076068
[TBL] [Abstract][Full Text] [Related]
7. A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field.
Chen M; Yan Q; Qin M
Comput Assist Surg (Abingdon); 2017 Dec; 22(sup1):200-211. PubMed ID: 29072503
[TBL] [Abstract][Full Text] [Related]
8. A new multistage medical segmentation method based on superpixel and fuzzy clustering.
Ji S; Wei B; Yu Z; Yang G; Yin Y
Comput Math Methods Med; 2014; 2014():747549. PubMed ID: 24734117
[TBL] [Abstract][Full Text] [Related]
9. Robust spatial fuzzy GMM based MRI segmentation and carotid artery plaque detection in ultrasound images.
Hassan M; Murtza I; Hira A; Ali S; Kifayat K
Comput Methods Programs Biomed; 2019 Jul; 175():179-192. PubMed ID: 31104706
[TBL] [Abstract][Full Text] [Related]
10. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.
Soltaninejad M; Yang G; Lambrou T; Allinson N; Jones TL; Barrick TR; Howe FA; Ye X
Int J Comput Assist Radiol Surg; 2017 Feb; 12(2):183-203. PubMed ID: 27651330
[TBL] [Abstract][Full Text] [Related]
11. MRI Brain Tumour Segmentation Using Hybrid Clustering and Classification by Back Propagation Algorithm.
M M; P S
Asian Pac J Cancer Prev; 2018 Nov; 19(11):3257-3263. PubMed ID: 30486629
[TBL] [Abstract][Full Text] [Related]
12. Multiscale superpixel method for segmentation of breast ultrasound.
Ilesanmi AE; Idowu OP; Makhanov SS
Comput Biol Med; 2020 Oct; 125():103879. PubMed ID: 32890977
[TBL] [Abstract][Full Text] [Related]
13. A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation.
Kaur T; Saini BS; Gupta S
Australas Phys Eng Sci Med; 2018 Mar; 41(1):41-58. PubMed ID: 29238919
[TBL] [Abstract][Full Text] [Related]
14. Robust kernelized local information fuzzy C-means clustering for brain magnetic resonance image segmentation.
Elazab A; AbdulAzeem YM; Wu S; Hu Q
J Xray Sci Technol; 2016 Mar; 24(3):489-507. PubMed ID: 27257884
[TBL] [Abstract][Full Text] [Related]
15. Effective fuzzy c-means based kernel function in segmenting medical images.
Kannan SR; Ramathilagam S; Sathya A; Pandiyarajan R
Comput Biol Med; 2010 Jun; 40(6):572-9. PubMed ID: 20444444
[TBL] [Abstract][Full Text] [Related]
16. Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation.
Li BN; Chui CK; Chang S; Ong SH
Comput Biol Med; 2011 Jan; 41(1):1-10. PubMed ID: 21074756
[TBL] [Abstract][Full Text] [Related]
17. A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection.
Kumar R; Srivastava S; Srivastava R
Comput Methods Programs Biomed; 2017 Jul; 146():59-68. PubMed ID: 28688490
[TBL] [Abstract][Full Text] [Related]
18. Brain Tumor Detection and Segmentation by Intensity Adjustment.
Rajan PG; Sundar C
J Med Syst; 2019 Jul; 43(8):282. PubMed ID: 31300899
[TBL] [Abstract][Full Text] [Related]
19. Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.
Blessy SA; Sulochana CH
Technol Health Care; 2015; 23(1):23-35. PubMed ID: 25408284
[TBL] [Abstract][Full Text] [Related]
20. Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.
Sauwen N; Acou M; Sima DM; Veraart J; Maes F; Himmelreich U; Achten E; Huffel SV
BMC Med Imaging; 2017 May; 17(1):29. PubMed ID: 28472943
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
