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Items: 1 to 20 of 129

1.

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 May-Jun;24(3):278-86. doi: 10.1111/jon.12000. Epub 2013 Feb 5.

PMID:
23384318
2.

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.

PMID:
20879221
3.

A MS-lesion pattern discrimination plot based on geostatistics.

Marschallinger R, Schmidt P, Hofmann P, Zimmer C, Atkinson PM, Sellner J, Trinka E, Mühlau M.

Brain Behav. 2016 Jan 30;6(3):e00430. doi: 10.1002/brb3.430. eCollection 2016 Mar.

4.

Generation of connectivity-preserving surface models of multiple sclerosis lesions.

Meruvia-Pastor O, Xiao M, Soh J, Sensen CW.

Stud Health Technol Inform. 2011;163:359-65.

PMID:
21335819
5.

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. doi: 10.1016/j.media.2010.12.003. Epub 2010 Dec 25.

PMID:
21233004
6.

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.

PMID:
24505804
7.

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.

PMID:
16685872
8.

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. doi: 10.1016/j.neurad.2014.05.006. Epub 2014 Jun 23.

9.

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.

PMID:
20426159
10.

A novel parametric method for non-rigid image registration.

Cuzol A, Hellier P, Mémin E.

Inf Process Med Imaging. 2005;19:456-67.

PMID:
17354717
11.

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. doi: 10.1016/j.mri.2008.01.016. Epub 2008 May 29.

PMID:
18513908
12.

Brain lesion detection in MRI with fuzzy and geostatistical models.

Pham TD.

Conf Proc IEEE Eng Med Biol Soc. 2010;2010:3150-3. doi: 10.1109/IEMBS.2010.5627188.

PMID:
21096593
13.

Reliability of longitudinal brain volume loss measurements between 2 sites in patients with multiple sclerosis: comparison of 7 quantification techniques.

Durand-Dubief F, Belaroussi B, Armspach JP, Dufour M, Roggerone S, Vukusic S, Hannoun S, Sappey-Marinier D, Confavreux C, Cotton F.

AJNR Am J Neuroradiol. 2012 Nov;33(10):1918-24. doi: 10.3174/ajnr.A3107. Epub 2012 Jul 12.

14.

Incorporating domain knowledge into the fuzzy connectedness framework: application to brain lesion volume estimation in multiple sclerosis.

Horsfield MA, Bakshi R, Rovaris M, Rocca MA, Dandamudi VS, Valsasina P, Judica E, Lucchini F, Guttmann CR, Sormani MP, Filippi M.

IEEE Trans Med Imaging. 2007 Dec;26(12):1670-80.

PMID:
18092737
15.

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.

PMID:
20879327
16.

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. doi: 10.1109/TMI.2012.2186639. Epub 2012 Feb 3.

PMID:
22318484
17.

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.

PMID:
23286071
18.

Accurate GM atrophy quantification in MS using lesion-filling with co-registered 2D lesion masks.

Popescu V, Ran NC, Barkhof F, Chard DT, Wheeler-Kingshott CA, Vrenken H.

Neuroimage Clin. 2014 Jan 18;4:366-73. doi: 10.1016/j.nicl.2014.01.004. eCollection 2014.

19.

In vivo quantitative evaluation of brain tissue damage in multiple sclerosis using gradient echo plural contrast imaging technique.

Sati P, Cross AH, Luo J, Hildebolt CF, Yablonskiy DA.

Neuroimage. 2010 Jul 1;51(3):1089-97. doi: 10.1016/j.neuroimage.2010.03.045. Epub 2010 Mar 23.

20.

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. doi: 10.1016/j.compmedimag.2010.02.001. Epub 2010 Feb 26.

PMID:
20189353

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