143 related articles for article (PubMed ID: 24505804)
1. Adaptive voxel, texture and temporal conditional random fields for detection of Gad-enhancing multiple sclerosis lesions in brain MRI.
Karimaghaloo Z; Rivaz H; Arnold DL; Collins DL; Arbel T
Med Image Comput Comput Assist Interv; 2013; 16(Pt 3):543-50. PubMed ID: 24505804
[TBL] [Abstract][Full Text] [Related]
2. Hierarchical conditional random fields for detection of gad-enhancing lesions in multiple sclerosis.
Karimaghaloo Z; Arnold DL; Collins DL; Arbel T
Med Image Comput Comput Assist Interv; 2012; 15(Pt 2):379-86. PubMed ID: 23286071
[TBL] [Abstract][Full Text] [Related]
3. Adaptive multi-level conditional random fields for detection and segmentation of small enhanced pathology in medical images.
Karimaghaloo Z; Arnold DL; Arbel T
Med Image Anal; 2016 Jan; 27():17-30. PubMed ID: 26211811
[TBL] [Abstract][Full Text] [Related]
4. Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields.
Karimaghaloo Z; Shah M; Francis SJ; Arnold DL; Collins DL; Arbel T
IEEE Trans Med Imaging; 2012 Jun; 31(6):1181-94. PubMed ID: 22318484
[TBL] [Abstract][Full Text] [Related]
5. Detection of Gad-enhancing lesions in multiple sclerosis using conditional random fields.
Karimaghaloo Z; Shah M; Francis SJ; Arnold DL; Collins DL; Arbel T
Med Image Comput Comput Assist Interv; 2010; 13(Pt 3):41-8. PubMed ID: 20879381
[TBL] [Abstract][Full Text] [Related]
6. Temporal Hierarchical Adaptive Texture CRF for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI.
Karimaghaloo Z; Rivaz H; Arnold DL; Collins DL; Arbel T
IEEE Trans Med Imaging; 2015 Jun; 34(6):1227-41. PubMed ID: 25532171
[TBL] [Abstract][Full Text] [Related]
7. IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI.
Subbanna N; Precup D; Arnold D; Arbel T
Inf Process Med Imaging; 2015; 24():514-26. PubMed ID: 26221699
[TBL] [Abstract][Full Text] [Related]
8. Spatial decision forests for MS lesion segmentation in multi-channel MR images.
Geremia E; Menze BH; Clatz O; Konukoglu E; Criminisi A; Ayache N
Med Image Comput Comput Assist Interv; 2010; 13(Pt 1):111-8. PubMed ID: 20879221
[TBL] [Abstract][Full Text] [Related]
9. A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images.
Khayati R; Vafadust M; Towhidkhah F; Nabavi SM
Comput Med Imaging Graph; 2008 Mar; 32(2):124-33. PubMed ID: 18055174
[TBL] [Abstract][Full Text] [Related]
10. Automated identification of brain new lesions in multiple sclerosis using subtraction images.
Battaglini M; Rossi F; Grove RA; Stromillo ML; Whitcher B; Matthews PM; De Stefano N
J Magn Reson Imaging; 2014 Jun; 39(6):1543-9. PubMed ID: 24987754
[TBL] [Abstract][Full Text] [Related]
11. Bayesian classification of multiple sclerosis lesions in longitudinal MRI using subtraction images.
Elliott C; Francis SJ; Arnold DL; Collins DL; Arbel T
Med Image Comput Comput Assist Interv; 2010; 13(Pt 2):290-7. PubMed ID: 20879327
[TBL] [Abstract][Full Text] [Related]
12. STREM: a robust multidimensional parametric method to segment MS lesions in MRI.
Aït-Ali LS; Prima S; Hellier P; Carsin B; Edan G; Barillot C
Med Image Comput Comput Assist Interv; 2005; 8(Pt 1):409-16. PubMed ID: 16685872
[TBL] [Abstract][Full Text] [Related]
13. Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome.
Loizou CP; Petroudi S; Seimenis I; Pantziaris M; Pattichis CS
J Neuroradiol; 2015 Apr; 42(2):99-114. PubMed ID: 24970463
[TBL] [Abstract][Full Text] [Related]
14. Usability and potential of geostatistics for spatial discrimination of multiple sclerosis lesion patterns.
Marschallinger R; Golaszewski SM; Kunz AB; Kronbichler M; Ladurner G; Hofmann P; Trinka E; McCoy M; Kraus J
J Neuroimaging; 2014; 24(3):278-86. PubMed ID: 23384318
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Quantification of global cerebral atrophy in multiple sclerosis from 3T MRI using SPM: the role of misclassification errors.
Dell'Oglio E; Ceccarelli A; Glanz BI; Healy BC; Tauhid S; Arora A; Saravanan N; Bruha MJ; Vartanian AV; Dupuy SL; Benedict RHB; Bakshi R; Neema M
J Neuroimaging; 2015; 25(2):191-199. PubMed ID: 25523616
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.
García-Lorenzo D; Lecoeur J; Arnold DL; Collins DL; Barillot C
Med Image Comput Comput Assist Interv; 2009; 12(Pt 2):584-91. PubMed ID: 20426159
[TBL] [Abstract][Full Text] [Related]
19. 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.
Yamamoto D; Arimura H; Kakeda S; Magome T; Yamashita Y; Toyofuku F; Ohki M; Higashida Y; Korogi Y
Comput Med Imaging Graph; 2010 Jul; 34(5):404-13. PubMed ID: 20189353
[TBL] [Abstract][Full Text] [Related]
20. Texture analysis of multiple sclerosis: a comparative study.
Zhang J; Tong L; Wang L; Li N
Magn Reson Imaging; 2008 Oct; 26(8):1160-6. PubMed ID: 18513908
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
