Johnston, B.,
Atkins, M.S.,
Mackiewich, B.T.,
Anderson, M.,
Segmentation of multiple sclerosis lesions in intensity corrected
multispectral MRI,
MedImg(15), No. 2, April 1996, pp. 154-169.
IEEE Top Reference.
0203
BibRef
Udupa, J.K.,
Wei, L.,
Samarasekera, S.,
Miki, Y.,
van Buchem, M.A.,
Grossman, R.I.,
Multiple sclerosis lesion quantification using fuzzy-connectedness
principles,
MedImg(16), No. 5, October 1997, pp. 598-609.
IEEE Top Reference.
0205
BibRef
van Leemput, K.,
Maes, F.,
Vandermeulen, D.,
Colchester, A.,
Suetens, P.,
Automated segmentation of multiple sclerosis lesions by model outlier
detection,
MedImg(20), No. 8, August 2001, pp. 677-688.
IEEE Top Reference.
0110
BibRef
Dehmeshki, J.,
Barker, G.J.,
Tofts, P.S.,
Classification of disease subgroup and correlation with disease
severity using magnetic resonance imaging whole-brain histograms: application
to magnetization transfer ratios and multiple sclerosis,
MedImg(21), No. 4, April 2002, pp. 320-331.
IEEE Top Reference.
0206
BibRef
Zijdenbos, A.P.,
Forghani, R.,
Evans, A.C.,
Automatic 'pipeline' analysis of 3-D MRI data for clinical trials:
application to multiple sclerosis,
MedImg(21), No. 10, October 2002, pp. 1280-1291.
IEEE Top Reference.
0301
BibRef
Horsfield, M.A.,
Bakshi, R.,
Rovaris, M.,
Rocca, M.A.,
Dandamudi, V.S.R.,
Valsasina, P.,
Judica, E.,
Lucchini, F.,
Guttmann, C.R.G.,
Sormani, M.P.,
Filippi, M.,
Incorporating Domain Knowledge Into the Fuzzy Connectedness Framework:
Application to Brain Lesion Volume Estimation in Multiple Sclerosis,
MedImg(26), No. 12, December 2007, pp. 1670-1680.
IEEE DOI
0712
BibRef
Garcia-Lorenzo, D.,
Prima, S.,
Arnold, D.L.,
Collins, D.L.,
Barillot, C.,
Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation
in Multisequence MRI for Multiple Sclerosis,
MedImg(30), No. 8, August 2011, pp. 1455-1467.
IEEE DOI
1108
BibRef
Karimaghaloo, Z.,
Shah, M.,
Francis, S.J.,
Arnold, D.L.,
Collins, D.L.,
Arbel, T.,
Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions
in Brain MRI Using Conditional Random Fields,
MedImg(31), No. 6, June 2012, pp. 1181-1194.
IEEE DOI
1206
BibRef
Beriault, S.,
Xiao, Y.M.[Yi-Ming],
Collins, D.L.,
Pike, G.B.,
Automatic SWI Venography Segmentation Using Conditional Random Fields,
MedImg(34), No. 12, December 2015, pp. 2478-2491.
IEEE DOI
1601
biomedical MRI
BibRef
Karimaghaloo, Z.,
Rivaz, H.,
Arnold, D.L.,
Collins, D.L.,
Arbel, T.,
Temporal Hierarchical Adaptive Texture CRF for Automatic Detection of
Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI,
MedImg(34), No. 6, June 2015, pp. 1227-1241.
IEEE DOI
1506
biomedical MRI
BibRef
Elliott, C.,
Arnold, D.L.,
Collins, D.L.,
Arbel, T.,
Temporally Consistent Probabilistic Detection of New Multiple
Sclerosis Lesions in Brain MRI,
MedImg(32), No. 8, 2013, pp. 1490-1503.
IEEE DOI
1307
Bayesian inference
BibRef
Tomas-Fernandez, X.,
Warfield, S.K.,
A Model of Population and Subject (MOPS) Intensities With Application
to Multiple Sclerosis Lesion Segmentation,
MedImg(34), No. 6, June 2015, pp. 1349-1361.
IEEE DOI
1506
biological tissues
BibRef
Brosch, T.[Tom],
Tang, L.Y.W.,
Yoo, Y.,
Li, D.K.B.[David K. B.],
Traboulsee, A.[Anthony],
Tam, R.[Roger],
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale
Feature Integration Applied to Multiple Sclerosis Lesion Segmentation,
MedImg(35), No. 5, May 2016, pp. 1229-1239.
IEEE DOI
1605
Convolution
BibRef
Yoo, Y.J.[Young-Jin],
Brosch, T.[Tom],
Traboulsee, A.[Anthony],
Li, D.K.B.[David K. B.],
Tam, R.[Roger],
Deep Learning of Image Features from Unlabeled Data for Multiple
Sclerosis Lesion Segmentation,
MLMI14(117-124).
Springer DOI
1410
BibRef
Strumia, M.,
Schmidt, F.R.,
Anastasopoulos, C.,
Granziera, C.,
Krueger, G.,
Brox, T.,
White Matter MS-Lesion Segmentation Using a Geometric Brain Model,
MedImg(35), No. 7, July 2016, pp. 1636-1646.
IEEE DOI
1608
biological tissues
BibRef
Wang, J.J.[Jing-Jing],
Hu, C.J.[Chang-Jun],
Xu, H.Q.A.[Hua-Qi-Ang],
Leng, Y.[Yan],
Zhang, L.[Liren],
Zhao, Y.F.[Yue-Feng],
A novel multi-atlas and multi-channel (MAMC) approach for multiple
sclerosis lesion segmentation in brain MRI,
SIViP(13), No. 5, July 2019, pp. 1019-1027.
Springer DOI
1906
BibRef
Sahnoun, M.[Mouna],
Kallel, F.[Fathi],
Dammak, M.[Mariem],
Kammoun, O.[Omar],
Mhiri, C.[Chokri],
Ben Mahfoudh, K.[Kheireddine],
Ben Hamida, A.[Ahmed],
Spinal cord MRI contrast enhancement using adaptive gamma correction
for patient with multiple sclerosis,
SIViP(14), No. 2, March 2020, pp. 377-385.
Springer DOI
2003
BibRef
Xiang, Y.[Yan],
Liu, H.[Han],
Wang, S.[Shuo],
Ma, L.[Lei],
Xiong, X.[Xin],
Xu, C.[Chunrong],
Shao, D.[Dangguo],
Segmentation method of multiple sclerosis lesions based on 3D-CNN
networks,
IET-IPR(14), No. 9, 20 July 2020, pp. 1806-1812.
DOI Link
2007
BibRef
Yildirim, M.S.[Mehmet Süleyman],
Dandil, E.[Emre],
Automatic detection of multiple sclerosis lesions using Mask R-CNN on
magnetic resonance scans,
IET-IPR(14), No. 16, 19 December 2020, pp. 4277-4290.
DOI Link
2103
BibRef
Soltani, A.[Azam],
Nasri, S.[Saeed],
Improved algorithm for multiple sclerosis diagnosis in MRI using
convolutional neural network,
IET-IPR(14), No. 17, 24 December 2020, pp. 4507-4512.
DOI Link
2104
BibRef
Alijamaat, A.[Ali],
NikravanShalmani, A.[Alireza],
Bayat, P.[Peyman],
Multiple sclerosis identification in brain MRI images using wavelet
convolutional neural networks,
IJIST(31), No. 2, 2021, pp. 778-785.
DOI Link
2105
CNN, deep learning, magnetic resonance imaging,
multiple sclerosis, wavelet
BibRef
Pandian, A.,
Udhayakumar, G.,
Improved multiple sclerosis diagnosis with advanced deep learning
techniques,
IJIST(33), No. 6, 2023, pp. 2128-2141.
DOI Link
2311
chaotic leader selective filler swarm optimization,
classification, early prediction,
multiple sclerosis
BibRef
Amaludin, B.[Bakhtiar],
Kadry, S.[Seifedine],
Ting, F.F.[Fung Fung],
Taniar, D.[David],
Toward more accurate diagnosis of multiple sclerosis: Automated
lesion segmentation in brain magnetic resonance image using modified
U-Net model,
IJIST(34), No. 1, 2024, pp. e22941.
DOI Link
2401
brain, deep learning, medical image, MRI,
MS lesion, segmentation, U-net
BibRef
Gregoriou, C.[Charalambos],
Loizou, C.P.[Christos P.],
Georgiou, A.[Andreas],
Pantzaris, M.[Marios],
Pattichis, C.S.[Constantinos S.],
A Three-Dimensional Reconstruction Integrated System for Brain Multiple
Sclerosis Lesions,
CAIP21(I:266-276).
Springer DOI
2112
BibRef
Nicolaou, A.[Andria],
Loizou, C.P.[Christos P.],
Pantzaris, M.[Marios],
Kakas, A.[Antonis],
Pattichis, C.S.[Constantinos S.],
Rule Extraction in the Assessment of Brain MRI Lesions in Multiple
Sclerosis: Preliminary Findings,
CAIP21(I:277-286).
Springer DOI
2112
BibRef
Georgiou, A.[Andreas],
Loizou, C.P.[Christos P.],
Nicolaou, A.[Andria],
Pantzaris, M.[Marios],
Pattichis, C.S.[Constantinos S.],
An Adaptive Semi-automated Integrated System for Multiple Sclerosis
Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional
Neural Network,
CAIP21(I:256-265).
Springer DOI
2112
BibRef
Yamin, M.A.[Muhammad Abubakar],
Valsasina, P.[Paola],
Dayan, M.[Michael],
Vascon, S.[Sebastiano],
Tessadori, J.[Jacopo],
Filippi, M.[Massimo],
Murino, V.[Vittorio],
Rocca, M.A.[Maria A.],
Sona, D.[Diego],
Encoding Brain Networks Through Geodesic Clustering of Functional
Connectivity for Multiple Sclerosis Classification,
ICPR21(10106-10112)
IEEE DOI
2105
Measurement, Support vector machines, Manifolds,
Multiple sclerosis, Dictionaries, Euclidean distance, Encoding
BibRef
Cruciani, F.[Federica],
Brusini, L.[Lorenza],
Zucchelli, M.[Mauro],
Pinheiro, G.R.[Gustavo Retuci],
Setti, F.[Francesco],
Galazzo, I.B.[Ilaria Boscolo],
Deriche, R.[Rachid],
Rittner, L.[Leticia],
Calabrese, M.[Massimiliano],
Menegaz, G.[Gloria],
Explainable 3D-CNN for Multiple Sclerosis Patients Stratification,
EDL-AI20(103-114).
Springer DOI
2103
BibRef
Ulloa, G.[Gustavo],
Veloz, A.[Alejandro],
Allende-Cid, H.[Héctor],
Allende, H.[Héctor],
Improving Multiple Sclerosis Lesion Boundaries Segmentation by
Convolutional Neural Networks with Focal Learning,
ICIAR20(II:182-192).
Springer DOI
2007
BibRef
Placidi, G.[Giuseppe],
Cinque, L.[Luigi],
Polsinelli, M.[Matteo],
Splendiani, A.[Alessandra],
Tommasino, E.[Emanuele],
Automatic Framework for Multiple Sclerosis Follow-up by Magnetic
Resonance Imaging for Reducing Contrast Agents,
CIAP19(II:367-378).
Springer DOI
1909
BibRef
Gong, Z.X.[Zhao-Xuan],
Zhao, D.[Dazhe],
Li, C.M.[Chun-Ming],
Tan, W.J.[Wen-Jun],
Davatzikos, C.[Christos],
A Robust Energy Minimization Algorithm for MS-Lesion Segmentation,
ISVC15(I: 521-530).
Springer DOI
1601
BibRef
Roy, S.[Snehashis],
Carass, A.[Aaron],
Prince, J.L.[Jerry L.],
Pham, D.L.[Dzung L.],
Longitudinal Patch-Based Segmentation of Multiple Sclerosis White
Matter Lesions,
MLMI15(194-202).
Springer DOI
1511
BibRef
Merzoug, A.,
Benamrane, N.,
Ahmed, A.T.,
MS lesions segmentation in 3D MR images using FCM and SVM,
IPTA14(1-5)
IEEE DOI
1503
biomedical MRI
BibRef
Meyer, A.[Anneke],
Multi-atlas Based Segmentation of Corpus Callosum on MRIs of Multiple
Sclerosis Patients,
GCPR14(729-735).
Springer DOI
1411
BibRef
Roy, P.K.[Pallab Kanti],
Bhuiyan, A.[Alauddin],
Ramamohanarao, K.[Kotagiri],
Automated segmentation of multiple sclerosis lesion in intensity
enhanced flair MRI using texture features and support vector machine,
ICIP13(4277-4281)
IEEE DOI
1402
Image Enhancement;Image Segmentation;MRI;Multiple Sclerosis;SVM
BibRef
Cabezas, M.[Mariano],
Oliver, A.[Arnau],
Freixenet, J.[Jordi],
Lladó, X.[Xavier],
A Supervised Approach for Multiple Sclerosis Lesion Segmentation Using
Context Features and an Outlier Map,
IbPRIA13(782-789).
Springer DOI
1307
BibRef
Strumia, M.[Maddalena],
Anastasopoulos, C.[Constantin],
Mader, I.[Irina],
Henning, J.[Jürgen],
Bai, L.[Li],
Hadjidemetriou, S.[Stathis],
Comparative Characterisation of Susceptibility Weighted MRI for Brain
White Matter Lesions in MS,
MBIA12(157-166).
Springer DOI
1210
BibRef
Mezgar, R.[Rabeb],
Mahjoub, M.A.[Mohamed Ali],
Salem, R.[Randa],
Mtibaa, A.[Abdellatif],
Brain MRI Image Segmentation in View of Tumor Detection: Application to
Multiple Sclerosis,
ICISP12(380-390).
Springer DOI
1208
BibRef
Lyksborg, M.[Mark],
Larsen, R.[Rasmus],
Sørensen, P.S.[Per Soelberg],
Blinkenberg, M.[Morten],
Garde, E.[Ellen],
Siebner, H.R.[Hartwig R.],
Dyrby, T.B.[Tim Bjørn],
Segmenting Multiple Sclerosis Lesions Using a Spatially Constrained
K-Nearest Neighbour Approach,
ICIAR12(II: 156-163).
Springer DOI
1206
BibRef
Sheikhzadeh, F.[Fahime],
Tam, R.[Roger],
Hamarneh, G.[Ghassan],
Spatial dispersion of lesions as a surrogate biomarker for disability
in multiple sclerosis,
MMBIA12(273-278).
IEEE DOI
1203
BibRef
Zeng, Z.M.[Zi-Ming],
Zwiggelaar, R.[Reyer],
Segmentation for Multiple Sclerosis Lesions Based on 3D Volume
Enhancement and 3D Alpha Matting,
ICIAR13(573-580).
Springer DOI
1307
BibRef
Earlier:
Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions,
MIRAGE11(133-144).
Springer DOI
1110
BibRef
Loizou, C.P.,
Murray, V.,
Pattichis, M.S.,
Pantziaris, M.,
Pattichis, C.S.,
AM-FM texture image analysis in brain white matter lesions in the
progression of Multiple Sclerosis,
Southwest10(61-64).
IEEE DOI
1005
BibRef
Liu, J.D.[Jun-Dong],
Smith, C.D.[Charles D.],
Chebrolu, H.[Himachandra],
Automatic Multiple Sclerosis detection based on integrated square
estimation,
MMBIA09(31-38).
IEEE DOI
0906
BibRef
Bricq, S.,
Collet, C.,
Armspach, J.P.,
Markovian segmentation of 3D brain MRI to detect Multiple Sclerosis
lesions,
ICIP08(733-736).
IEEE DOI
0810
BibRef
Harmouche, R.[Rola],
Collins, L.[Louis],
Arnold, D.[Douglas],
Francis, S.[Simon],
Arbel, T.[Tal],
Bayesian MS Lesion Classification Modeling Regional and Local Spatial
Information,
ICPR06(III: 984-987).
IEEE DOI
0609
BibRef
Akselrod-Ballin, A.[Ayelet],
Galun, M.[Meirav],
Basri, R.[Ronen],
Brandt, A.[Achi],
Gomori, M.J.[Moshe John],
Filippi, M.[Massimo],
Valsasina, P.[Paula],
An Integrated Segmentation and Classification Approach Applied to
Multiple Sclerosis Analysis,
CVPR06(I: 1122-1129).
IEEE DOI
0606
BibRef
Admasu, F.,
Al-Zubi, S.,
Toennies, K.D.,
Bodammer, N.,
Hinrichs, H.,
Segmentation of multiple sclerosis lesions from mr brain images using
the principles of fuzzy-connectedness and artificial neuron networks,
ICIP03(II: 1081-1084).
IEEE DOI
0312
BibRef
Ardizzone, E.[Edoardo],
Pirrone, R.[Roberto],
Gambino, O.[Orazio],
Peri, D.,
Two channels fuzzy c-means detection of multiple sclerosis lesions in
multispectral MR images,
ICIP02(II: 345-348).
IEEE DOI
0210
BibRef
Rey, D.,
Stoeckel, J.,
Malandain, G.,
Ayache, N.J.,
A Spatio-Temporal Model-Based Statistical Approach to Detect Evolving
Multiple Sclerosis Lesions,
MMBIA01(xx-yy).
0110
BibRef
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