265 related articles for article (PubMed ID: 29763764)
21. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.
Zhang X; Yan LF; Hu YC; Li G; Yang Y; Han Y; Sun YZ; Liu ZC; Tian Q; Han ZY; Liu LD; Hu BQ; Qiu ZY; Wang W; Cui GB
Oncotarget; 2017 Jul; 8(29):47816-47830. PubMed ID: 28599282
[TBL] [Abstract][Full Text] [Related]
22. Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.
Deng M; Yu R; Wang L; Shi F; Yap PT; Shen D;
Med Phys; 2016 Dec; 43(12):6588-6597. PubMed ID: 28054724
[TBL] [Abstract][Full Text] [Related]
23. Texture based localization of a brain tumor from MR-images by using a machine learning approach.
Rehman ZU; Zia MS; Bojja GR; Yaqub M; Jinchao F; Arshid K
Med Hypotheses; 2020 Aug; 141():109705. PubMed ID: 32289646
[TBL] [Abstract][Full Text] [Related]
24. 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]
25. An Efficient Optimization Approach for Glioma Tumor Segmentation in Brain MRI.
Barzegar Z; Jamzad M
J Digit Imaging; 2022 Dec; 35(6):1634-1647. PubMed ID: 35995900
[TBL] [Abstract][Full Text] [Related]
26. Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: a preliminary investigation in terms of identification and segmentation.
Assefa D; Keller H; Ménard C; Laperriere N; Ferrari RJ; Yeung I
Med Phys; 2010 Apr; 37(4):1722-36. PubMed ID: 20443493
[TBL] [Abstract][Full Text] [Related]
27. Automated brain tumor segmentation from multi-slices FLAIR MRI images.
Eltayeb EN; Salem NM; Al-Atabany W
Biomed Mater Eng; 2019; 30(4):449-462. PubMed ID: 31476145
[TBL] [Abstract][Full Text] [Related]
28. Brain tumor segmentation with Vander Lugt correlator based active contour.
Essadike A; Ouabida E; Bouzid A
Comput Methods Programs Biomed; 2018 Jul; 160():103-117. PubMed ID: 29728237
[TBL] [Abstract][Full Text] [Related]
29. Quantitative glioma grading using transformed gray-scale invariant textures of MRI.
Li-Chun Hsieh K; Chen CY; Lo CM
Comput Biol Med; 2017 Apr; 83():102-108. PubMed ID: 28254615
[TBL] [Abstract][Full Text] [Related]
30. Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.
Pereira S; Meier R; McKinley R; Wiest R; Alves V; Silva CA; Reyes M
Med Image Anal; 2018 Feb; 44():228-244. PubMed ID: 29289703
[TBL] [Abstract][Full Text] [Related]
31. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.
Naceur MB; Saouli R; Akil M; Kachouri R
Comput Methods Programs Biomed; 2018 Nov; 166():39-49. PubMed ID: 30415717
[TBL] [Abstract][Full Text] [Related]
32. Brain tumor classification and segmentation using sparse coding and dictionary learning.
Salman Al-Shaikhli SD; Yang MY; Rosenhahn B
Biomed Tech (Berl); 2016 Aug; 61(4):413-29. PubMed ID: 26351901
[TBL] [Abstract][Full Text] [Related]
33. Automated glioma detection and segmentation using graphical models.
Zhao Z; Yang G; Lin Y; Pang H; Wang M
PLoS One; 2018; 13(8):e0200745. PubMed ID: 30130371
[TBL] [Abstract][Full Text] [Related]
34. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI.
Sauwen N; Sima DM; Van Cauter S; Veraart J; Leemans A; Maes F; Himmelreich U; Van Huffel S
NMR Biomed; 2015 Dec; 28(12):1599-624. PubMed ID: 26458729
[TBL] [Abstract][Full Text] [Related]
35. Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.
Zhan T; Chen Y; Hong X; Lu Z; Chen Y
CNS Neurol Disord Drug Targets; 2017; 16(2):129-136. PubMed ID: 28088902
[TBL] [Abstract][Full Text] [Related]
36. Brain tumor segmentation from multimodal magnetic resonance images via sparse representation.
Li Y; Jia F; Qin J
Artif Intell Med; 2016 Oct; 73():1-13. PubMed ID: 27926377
[TBL] [Abstract][Full Text] [Related]
37. Fusion based Glioma brain tumor detection and segmentation using ANFIS classification.
Selvapandian A; Manivannan K
Comput Methods Programs Biomed; 2018 Nov; 166():33-38. PubMed ID: 30415716
[TBL] [Abstract][Full Text] [Related]
38. Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.
Dera D; Bouaynaya N; Fathallah-Shaykh HM
Bull Math Biol; 2016 Jul; 78(7):1450-76. PubMed ID: 27417984
[TBL] [Abstract][Full Text] [Related]
39. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.
Kickingereder P; Isensee F; Tursunova I; Petersen J; Neuberger U; Bonekamp D; Brugnara G; Schell M; Kessler T; Foltyn M; Harting I; Sahm F; Prager M; Nowosielski M; Wick A; Nolden M; Radbruch A; Debus J; Schlemmer HP; Heiland S; Platten M; von Deimling A; van den Bent MJ; Gorlia T; Wick W; Bendszus M; Maier-Hein KH
Lancet Oncol; 2019 May; 20(5):728-740. PubMed ID: 30952559
[TBL] [Abstract][Full Text] [Related]
40. A hybrid feature selection-based brain tumor detection and segmentation in multiparametric magnetic resonance imaging.
Chen H; Ban D; Qi XS; Pan X; Qiang Y; Yang Q
Med Phys; 2021 Nov; 48(11):7360-7371. PubMed ID: 34101842
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]