20.9.9 Multiple Sclerosis Detection and Analysis

Chapter Contents (Back)
Multiple Sclerosis.

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


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.[Ziming], 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

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Epileptic Seizures, Epilepsy Detection and Analysis .


Last update:Sep 25, 2017 at 16:36:46