21.9.7 Brain, Cortex, MRI Analysis, Models, 3-D

Chapter Contents (Back)
Brain. Cortex. MRI. Brain MRI.
See also Brain, Cortex, Registration, Alignment, MRI, Other.
See also White Matter Fiber Tractography MRI.
See also Functional Magnetic Resonance, fMRI. Segmentation specific papers in:
See also Brain, Cortex, MRI Segmentation. Much of it is MRI Based:
See also Brain Tumors, Cortex, Cancer.

Raman, S.V., Sarkar, S., Boyer, K.L.,
Hypothesizing Structures in Edge-Focused Cerebral Magnetic Resonance Images Using Graph-Theoretic Cycle Enumeration,
CVGIP(57), No. 1, January 1993, pp. 81-98.
DOI Link BibRef 9301

Ge, Y., Fitzpatrick, J.M., Dawant, B.M., Bao, J., Kessler, R.M., Margolin, R.A.,
Accurate localization of cortical convolutions in MR brain images,
MedImg(15), No. 4, August 1996, pp. 418-428.
IEEE Top Reference. 0203
BibRef

Sonka, M., Tadikonda, S.K., Collins, S.M.,
Knowledge-based interpretation of MR brain images,
MedImg(15), No. 4, August 1996, pp. 443-452.
IEEE Top Reference. 0203
BibRef

Gosche, K.M.[Karen M.], Velthuizen, R.P.[Robert P.], Murtagh, F.R.[F. Reed], Arrington, J.A.[John A.], Gross, W.W.[William W.], Mortimer, J.A.[James A.], Clarke, L.P.[Laurence P.],
Automated quantification of brain magnetic resonance image hyperintensities using hybrid clustering and knowledge-based methods,
IJIST(10), No. 3, 1999, pp. 287-293. BibRef 9900

Wang, Y., Adali, T., Lau, C.M., Kung, S.Y.,
Quantitative-Analysis of MR Brain Image Sequences by Adaptive Self-Organizing Finite Mixtures,
VLSIVideo(18), No. 3, April 1998, pp. 219-239. 9806
BibRef

Zengingonul, H.P.[Hale Pinar], Mulkern, R.V.[Robert V.],
Measurement and analysis of nonexponential signal decay curves in brain diffusion and muscle relaxation magnetic resonance studies in humans,
IJIST(10), No. 3, 1999, pp. 294-303. BibRef 9900

Thulborn, K.R.[Keith R.], Uttecht, S.D.[Steve D.],
Volumetry and topography of the human brain by magnetic resonance imaging,
IJIST(11), No. 3, 2000, pp. 198-208. 0102
BibRef

Xu, C.Y., Pham, D.L., Rettmann, M.E., Yu, D.N., Prince, J.L.,
Reconstruction of the human cerebral cortex from magnetic resonance images,
MedImg(18), No. 6, June 1999, pp. 467-480.
IEEE Top Reference. 0110

See also Topology Preserving Level Set Method for Geometric Deformable Models. BibRef

Han, X., Xu, C.Y., Tosun, D., Prince, J.L.,
Cortical Surface Reconstruction Using a Topology Preserving Geometric Deformable Model,
MMBIA01(xx-yy). 0110
Cortex. BibRef

Pham, D.L., Prince, J.L.,
Partial volume estimation and the fuzzy C-means algorithm,
ICIP98(III: 819-822).
IEEE DOI 9810
brain MRI application BibRef

Bullmore, E.T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., Brammer, M.J.,
Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain,
MedImg(18), No. 1, January 1999, pp. 32-42.
IEEE Top Reference. 0110
BibRef

Amit, Y.,
Graphical shape templates for automatic anatomy detection with applications to MRI brain scans,
MedImg(16), No. 1, February 1997, pp. 28-40.
IEEE Top Reference. 0205
BibRef

Bao, Y.F.[Yu-Fang], Maudsley, A.A.[Andrew A.],
Improved Reconstruction for MR Spectroscopic Imaging,
MedImg(26), No. 5, May 2007, pp. 686-695.
IEEE DOI 0705
BibRef

Windreich, G., Kiryati, N., Lohmann, G.[Gabriele],
Voxel-based surface area estimation: from theory to practice,
PR(36), No. 11, November 2003, pp. 2531-2541.
Elsevier DOI 0309
An issue for MRI. BibRef

Lohmann, G.[Gabriele], von Cramon, D.Y.[D. Yves],
Automatic Detection and Labelling of the Human Cortical Folds in Magnetic Resonance Data Sets,
ECCV98(II: 369).
Springer DOI BibRef 9800

Drapaca, C.S.[Corina S.], Cardenas, V.[Valerie], Studholme, C.[Colin],
Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets,
CVIU(100), No. 3, December 2005, pp. 312-329.
Elsevier DOI 0512
BibRef

Studholme, C., Drapaca, C.S., Iordanova, B., Cardenas, V.,
Deformation-Based Mapping of Volume Change From Serial Brain MRI in the Presence of Local Tissue Contrast Change,
MedImg(25), No. 5, May 2006, pp. 626-639.
IEEE DOI 0605
BibRef

Koretz, J.F.[Jane F.], Strenk, S.A.[Susan A.], Strenk, L.M.[Lawrence M.], Semmlow, J.L.[John L.],
Scheimpflug and high-resolution Magnetic Resonance Imaging of the anterior segment: a comparative study,
JOSA-A(21), No. 3, March 2004, pp. 346-354.
WWW Link. 0409
BibRef

Sajda, P., Du, S., Brown, T.R., Stoyanova, R.S., Shungu, D.C., Mao, X.L.[Xiang-Ling], Parra, L.C.,
Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain,
MedImg(23), No. 12, December 2004, pp. 1453-1465.
IEEE Abstract. 0412
BibRef

Barta, P., Miller, M.I., Qiu, A.,
A Stochastic Model for Studying the Laminar Structure of Cortex From MRI,
MedImg(24), No. 6, June 2005, pp. 728-742.
IEEE Abstract. 0506
BibRef

Wismüller, A.[Axel], Meyer-Baese, A., Lange, O.[Oliver], Reiser, M.F., Leinsinger, G.L.[Gerda L.],
Cluster Analysis of Dynamic Cerebral Contrast-Enhanced Perfusion MRI Time-Series,
MedImg(25), No. 1, January 2006, pp. 62-73.
IEEE DOI 0601

See also Cluster Analysis of Biomedical Image Time-Series. BibRef

Bach Cuadra, M., Cammoun, L., Butz, T., Cuisenaire, O., Thiran, J.P.,
Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images,
MedImg(24), No. 12, December 2005, pp. 1548-1565.
IEEE DOI 0601
BibRef
And: Correction: MedImg(25), No. 2, February 2006, pp. 241-241.
IEEE DOI 0602
BibRef

Linguraru, M.G., Ayache, N.J., Bardinet, E., Gonzalez Ballester, M.A., Galanaud, D., Haik, S., Faucheux, B., Hauw, J.J., Cozzone, P., Dormont, D., Brandel, J.P.,
Differentiation of sCJD and vCJD Forms by Automated Analysis of Basal Ganglia Intensity Distribution in Multisequence MRI of the Brain: Definition and Evaluation of New MRI-Based Ratios,
MedImg(25), No. 8, August 2006, pp. 1052-1067.
IEEE DOI 0608
BibRef

Sebastiani, G.[Giovanni], de Pasquale, F.[Francesco], Barone, P.[Piero],
Quantifying Human Brain Connectivity from Diffusion Tensor MRI,
JMIV(25), No. 2, September 2006, pp. 227-244.
Springer DOI 0610

See also Solving an Inverse Diffusion Problem for Magnetic Resonance Dosimetry by a Fast Regularization Method. BibRef

Schmid, V.J., Whitcher, B., Padhani, A.R., Taylor, N.J., Yang, G.Z.,
Bayesian Methods for Pharmacokinetic Models in Dynamic Contrast-Enhanced Magnetic Resonance Imaging,
MedImg(25), No. 12, December 2006, pp. 1627-1636.
IEEE DOI 0701
BibRef

Schmid, V.J., Whitcher, B., Padhani, A.R., Yang, G.Z.,
Quantitative Analysis of Dynamic Contrast-Enhanced MR Images Based on Bayesian P-Splines,
MedImg(28), No. 6, June 2009, pp. 789-798.
IEEE DOI 0906
BibRef

Thomaz, C.E.[Carlos E.], Duran, F.L.S.[Fabio L. S.], Busatto, G.F.[Geraldo F.], Gillies, D.F.[Duncan F.], Rueckert, D.[Daniel],
Multivariate Statistical Differences of MRI Samples of the Human Brain,
JMIV(29), No. 2-3, November 2007, pp. 95-106.
Springer DOI 0712
BibRef

Lin, L.[Lei], Zhu, L.T.[Li-Tao], Yang, F.[Faguo], Jiang, T.Z.[Tian-Zi],
A Novel Pixon-Representation for Image Segmentation Based on Markov Random Field,
IVC(26), No. 11, 1 November 2008, pp. 1507-1514.
Elsevier DOI 0804
Image segmentation; Pixon-representation; Markov random field; Region labeling
See also Pixon-Based Image Denoising with Markov Random Fields. BibRef

Yang, F.[Faguo], Jiang, T.Z.[Tian-Zi],
Pixon-based image segmentation with Markov random fields,
IP(12), No. 12, December 2003, pp. 1552-1559.
IEEE DOI 0402

See also Pixon-Based Image Denoising with Markov Random Fields. BibRef

Lin, L.[Lei], Garcia-Lorenzo, D.[Daniel], Li, C.[Chong], Jiang, T.Z.[Tian-Zi], Barillot, C.[Christian],
Adaptive Pixon Represented Segmentation (APRS) for 3D MR Brain Images Based on Mean Shift and Markov Random Fields,
PRL(32), No. 7, 1 May 2011, pp. 1036-1043.
Elsevier DOI 1101
MRI segmentation; Markov random field; Adaptive mean shift; Pixon-representation; EM algorithm BibRef

Schaer, M., Cuadra, M.B., Tamarit, L., Lazeyras, F., Eliez, S., Thiran, J.P.,
A Surface-Based Approach to Quantify Local Cortical Gyrification,
MedImg(27), No. 2, February 2008, pp. 161-170.
IEEE DOI 0802
BibRef

Pohl, K.M., Bouix, S., Nakamura, M., Rohlfing, T., McCarley, R.W., Kikinis, R., Grimson, W.E.L., Shenton, M.E., Wells, III, W.M.[William M.],
A Hierarchical Algorithm for MR Brain Image Parcellation,
MedImg(26), No. 9, September 2007, pp. 1201-1212.
IEEE DOI 0710
BibRef

Toews, M.[Matthew], Wells, W.M.[William M.],
A mutual-information scale-space for image feature detection and feature-based classification of volumetric brain images,
MMBIA10(111-116).
IEEE DOI 1006
BibRef

Pohl, K.M.[Kilian M.], Fisher, J.W.[John W.], Kikinis, R.[Ron], Grimson, W.E.L.[W. Eric L.], Wells, III, W.M.[William M.],
Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images,
CVBIA05(489-498).
Springer DOI 0601
BibRef
And:
An Expectation Maximization Approach for Integrated Registration, Segmentation, and Intensity Correction,
CSAIL-2005-020, April 2005.
WWW Link. BibRef

Bose, B.[Biswajit], Fisher, J.W.[John W.], Fischl, B.[Bruce], Hinds, O.[Oliver], Grimson, W.E.L.[W. Eric L.],
Detecting Cortical Surface Regions in Structural MR Data,
MMBIA07(1-8).
IEEE DOI 0710
BibRef

Cho, Z.H.[Zang-Hee], Kim, Y.B.[Young-Bo], Han, J.Y.[Jae-Yong], Min, H.K.[Hoon-Ki], Kim, K.N.[Kyoung-Nam], Choi, S.H.[Sang-Han], Veklerov, E.[Eugene], Shepp, L.A.[Larry A.],
New brain atlas: Mapping the human brain in vivo with 7.0 T MRI and comparison with postmortem histology: Will these images change modern medicine?,
IJIST(18), No. 1, 2008, pp. 2-8.
DOI Link 0806
BibRef

Kwon, M.J.[Min Jeong], Hahn, J.Y.[Joo-Young], Park, H.W.[Hyun-Wook],
A fast spherical inflation method of the cerebral cortex by deformation of a simplex mesh on the polar coordinates,
IJIST(18), No. 1, 2008, pp. 9-16.
DOI Link 0806
BibRef

Marzelli, M.[Matthew], Fischer, K.[Krisztina], Kim, Y.B.[Young Beom], Mulkern, R.V.[Robert V.], Yoo, S.S.[Seung-Schik], Park, H.W.[Hyun-Wook], Cho, Z.H.[Zang-Hee],
Composite MR contrast agents for conditional cell-labeling,
IJIST(18), No. 1, 2008, pp. 79-84.
DOI Link 0806
BibRef

Meegama, R.G.N.[Ravinda G. N.], Rajapakse, J.C.[Jagath C.],
Fully Automated Peeling Technique For T1-weighted, High-quality MR Head Scans,
IJIG(4), No. 2, April 2004, pp. 141-156. 0404
BibRef

Tosun, D., Prince, J.L.,
A Geometry-Driven Optical Flow Warping for Spatial Normalization of Cortical Surfaces,
MedImg(27), No. 12, December 2008, pp. 1739-1753.
IEEE DOI 0812
BibRef

Paley, M.N.J.[Martyn N.J.], Chow, L.S.[Li Sze], Whitby, E.H.[Elspeth H.], Cook, G.G.[Greg G.],
Modelling of axonal fields in the optic nerve for direct MR detection studies,
IVC(27), No. 4, 3 March 2009, pp. 331-341.
Elsevier DOI 0804
Optic nerve; Axonal waveform; Direct MR detection; Neuronal firing; Axonal firing; Hodgkin-Huxley equations; Direct neuronal detection; MRI BibRef

Koo, J.J., Evans, A.C., Gross, W.J.,
3-D Brain MRI Tissue Classification on FPGAs,
IP(18), No. 12, December 2009, pp. 2735-2746.
IEEE DOI 0912
BibRef

Park, J.S., Chung, M.S., Shin, D.S., Har, D.H., Cho, Z.H., Kim, Y.B., Han, J.Y., Chi, J.G.,
Sectioned Images of the Cadaver Head Including the Brain and Correspondences With Ultrahigh Field 7.0 T MRIs,
PIEEE(97), No. 12, December 2009, pp. 1988-1996.
IEEE DOI 0912
BibRef

Beekman, F.J., Vastenhouw, B., van der Wilt, G., Vervloet, M., Visscher, R., Booij, J., Gerrits, M., Ji, C., Ramakers, R., van der Have, F.,
3-D Rat Brain Phantom for High-Resolution Molecular Imaging,
PIEEE(97), No. 12, December 2009, pp. 1997-2005.
IEEE DOI 0912
BibRef

DeLorenzo, C.[Christine], Papademetris, X.[Xenophon], Staib, L.H.[Lawrence H.], Vives, K.P.[Kenneth P.], Spencer, D.D.[Dennis D.], Duncan, J.S.[James S.],
Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation,
MedImg(31), No. 8, August 2012, pp. 1607-1619.
IEEE DOI 1208
BibRef

Lopes, R., Dubois, P., Bhouri, I., Bedoui, M.H., Maouche, S., Betrouni, N.,
Local fractal and multifractal features for volumic texture characterization,
PR(44), No. 8, August 2011, pp. 1690-1697.
Elsevier DOI 1104
3D fractal; Multifractal analysis; Texture analysis; Classification BibRef

Angelone, L.M., Ahveninen, J., Belliveau, J.W., Bonmassar, G.,
Analysis of the Role of Lead Resistivity in Specific Absorption Rate for Deep Brain Stimulator Leads at 3T MRI,
MedImg(29), No. 4, April 2010, pp. 1029-1038.
IEEE DOI 1003
BibRef

Li, Y., Gilmore, J.H., Wang, J., Styner, M., Lin, W., Zhu, H.,
TwinMARM: Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data,
MedImg(31), No. 5, May 2012, pp. 1100-1112.
IEEE DOI 1202
BibRef

Isoardi, R.A., Oliva, D.E., Mato, G.,
Maximum Evidence Method for classification of brain tissues in MRI,
PRL(31), No. 1, January 2010, pp. 12-18.
Elsevier DOI 1011
Bayesian estimation; Magnetic Resonance Imaging; Image segmentation; Partial volume effect BibRef

Castro, M.A., Yao, J., Pang, Y., Lee, C., Baker, E., Butman, J., Evangelou, I.E., Thomasson, D.,
Template-Based B_1 Inhomogeneity Correction in 3T MRI Brain Studies,
MedImg(29), No. 11, November 2010, pp. 1927-1941.
IEEE DOI 1011
BibRef

Gholipour, A., Estroff, J.A., Warfield, S.K.,
Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI,
MedImg(29), No. 10, October 2010, pp. 1739-1758.
IEEE DOI 1011

See also Symmetric deformable image registration via optimization of information theoretic measures. BibRef

Scherrer, B.[Benoit], Gholipour, A.[Ali], Warfield, S.K.[Simon K.],
Super-resolution reconstruction of diffusion-weighted images from distortion compensated orthogonal anisotropic acquisitions,
MMBIA12(249-254).
IEEE DOI 1203
BibRef

Yousefi, S., Kehtarnavaz, N., Gholipour, A., Gopinath, K.S., Briggs, R.W.,
Comparison of atlas-based segmentation of subcortical structures in magnetic resonance brain images,
Southwest10(1-4).
IEEE DOI 1005
BibRef

Ceyhan, E., Hosakere, M., Nishino, T., Alexopoulos, J., Todd, R.D., Botteron, K.N., Miller, M.I., Ratnanather, J.T.,
Statistical Analysis of Cortical Morphometrics Using Pooled Distances Based on Labeled Cortical Distance Maps,
JMIV(40), No. 1, May 2011, pp. 20-35.
WWW Link. 1103
BibRef

Bhatia, K.K., Rao, A., Price, A.N., Wolz, R., Hajnal, J.V., Rueckert, D.,
Hierarchical Manifold Learning for Regional Image Analysis,
MedImg(33), No. 2, February 2014, pp. 444-461.
IEEE DOI 1403
biomedical MRI BibRef

Mendiola-Santibañez, J.D.[Jorge D.], Terol-Villalobos, I.R.[Iván R.], Jiménez-Sánchez, A.R.[Angélica R.], Gallegos-Duarte, M.[Martín], Rodriguez-Resendiz, J.[Juvenal], Santillan, I.[Israel],
Application of morphological connected openings and levelings on magnetic resonance images of the brain,
IJIST(21), No. 4, December 2011, pp. 336-348.
DOI Link 1112
BibRef

Cerrolaza, J.J., Villanueva, A., Cabeza, R.,
Hierarchical Statistical Shape Models of Multiobject Anatomical Structures: Application to Brain MRI,
MedImg(31), No. 3, March 2012, pp. 713-724.
IEEE DOI 1203
BibRef

Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.,
Review of brain MRI image segmentation methods,
AIR(33), No. 3, March 2010, pp. 261-274.
WWW Link. 1208
Survey, MRI. BibRef

Balafar, M.A., Ramli, A.R., Mashohor, S.,
A new method for MR grayscale inhomogeneity correction,
AIR(34), No. 2, August 2010, pp. 195-204.
WWW Link. 1208
BibRef

Elsayed, A.[Ashraf], Coenen, F.[Frans], García-Fiñana, M.[Marta], Sluming, V.[Vanessa],
Classification of MRI Brain Scan Data Using Shape Criteria,
BMVA(2011), No. 6, 2011, pp. 1-14.
PDF File. 1209
BibRef

Mace, E., Cohen, I., Montaldo, G., Miles, R., Fink, M., Tanter, M.,
In Vivo Mapping of Brain Elasticity in Small Animals Using Shear Wave Imaging,
MedImg(30), No. 3, March 2011, pp. 550-558.
IEEE DOI 1103
BibRef

Deligianni, F.[Fani], Robinson, E.C.[Emma Claire], Edwards, A.D.[A. David], Rueckert, D.[Daniel], Sharp, D.[David], Alexander, D.[Daniel],
Hierarchy in Anatomical Brain Networks Derived from Diffusion Weighted Images in 64 and 15 Directions,
BMVA(2012), No. 4, 2012, pp. 1-21.
PDF File. 1209
BibRef

Auzias, G., Le Fevre, J., Le Troter, A., Fisher, C., Perrot, M., Regis, J., Coulon, O.,
Model-Driven Harmonic Parameterization of the Cortical Surface: HIP-HOP,
MedImg(32), No. 5, May 2013, pp. 873-887.
IEEE DOI 1305
BibRef

Zhang, X., Schmitter, S., van de Moortele, P.F., Liu, J., He, B.,
From Complex B_1 Mapping to Local SAR Estimation for Human Brain MR Imaging Using Multi-Channel Transceiver Coil at 7T,
MedImg(32), No. 6, 2013, pp. 1058-1067.
IEEE DOI 1307
Head; Magnetic resonance imaging; magnetic resonance imaging (MRI); BibRef

Liu, J., van de Moortele, P.F., Zhang, X., Wang, Y., He, B.,
Simultaneous Quantitative Imaging of Electrical Properties and Proton Density From B_1 Maps Using MRI,
MedImg(35), No. 9, September 2016, pp. 2064-2073.
IEEE DOI 1609
Load modeling BibRef

Wang, Y., van de Moortele, P., He, B.,
CONtrast Conformed Electrical Properties Tomography (CONCEPT) Based on Multi- Channel Transmission and Alternating Direction Method of Multipliers,
MedImg(38), No. 2, February 2019, pp. 349-359.
IEEE DOI 1902
Image reconstruction, Radio frequency, Magnetic resonance imaging, Tomography, alternating direction method of multipliers (ADMM) BibRef

O'Brien, K., Daducci, A., Kickler, N., Lazeyras, F., Gruetter, R., Feiweier, T., Krueger, G.,
3-D Residual Eddy Current Field Characterisation: Applied to Diffusion Weighted Magnetic Resonance Imaging,
MedImg(32), No. 8, 2013, pp. 1515-1525.
IEEE DOI 1308
Brain BibRef

Rouchdy, Y.[Youssef], Cohen, L.D.[Laurent D.],
Geodesic voting for the automatic extraction of tree structures. Methods and applications,
CVIU(117), No. 10, 2013, pp. 1453-1467.
Elsevier DOI 1309
BibRef
Earlier:
The shading zone problem in geodesic voting and its solutions for the segmentation of tree structures. Application to the segmentation of Microglia extensions,
MMBIA09(66-71).
IEEE DOI 0906
BibRef
Earlier:
Image segmentation by geodesic voting. Application to the extraction of tree structures from confocal microscope images,
ICPR08(1-5).
IEEE DOI 0812
Geodesic voting BibRef

Cohen, L.D.,
Geodesic methods for biomedical image segmentation,
IPTA14(1-1)
IEEE DOI 1503
differential geometry BibRef

Iftikhar, M.A.[Muhammad Aksam], Jalil, A.[Abdul], Rathore, S.[Saima], Ali, A.[Ahmad], Hussain, M.[Mutawarra],
Brain MRI denoising and segmentation based on improved adaptive nonlocal means,
IJIST(23), No. 3, 2013, pp. 235-248.
DOI Link 1309
nonlocal means, adaptive, denoizing, brain MRI, segmentation BibRef

Iftikhar, M.A.[Muhammad Aksam], Jalil, A.[Abdul], Rathore, S.[Saima], Ali, A.[Ahmad], Hussain, M.[Mutawarra],
An extended non-local means algorithm: Application to brain MRI,
IJIST(24), No. 4, 2014, pp. 293-305.
DOI Link 1411
nonlocal means, denoising, brain MRI, Rician noise, wavelet BibRef

Iftikhar, M.A.[Muhammad Aksam], Jalil, A.[Abdul], Rathore, S.[Saima], Hussain, M.[Mutawarra],
Robust brain MRI denoising and segmentation using enhanced non-local means algorithm,
IJIST(24), No. 1, 2014, pp. 52-66.
DOI Link 1403
non-local means, denoising, brain MRI, segmentation, Rician noise BibRef

Saritha, M., Joseph, K.P.[K. Paul], Mathew, A.T.[Abraham T.],
Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network,
PRL(34), No. 16, 2013, pp. 2151-2156.
Elsevier DOI 1310
Magnetic resonance imaging (MRI) BibRef

Priya, R.K.[R. Krishna], Thangaraj, C., Kesavadas, C., Kannan, S.,
Fuzzy entropy-based MR brain image segmentation using modified particle swarm optimization,
IJIST(23), No. 4, 2013, pp. 281-288.
DOI Link 1312
fuzzy entropy, Magnetic Resonance Image, Fuzzy Membership function BibRef

Bianchi, A.[Anthony], Bhanu, B.[Bir], Donovan, V.[Virginia], Obenaus, A.[Andre],
Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury,
MedImg(33), No. 1, January 2014, pp. 11-22.
IEEE DOI 1402
BibRef
And:
Detecting mild traumatic brain injury using dynamic low level context,
ICIP13(1167-1171)
IEEE DOI 1402
BibRef
Earlier:
Contextual and visual modeling for detection of mild traumatic brain injury in MRI,
ICIP12(1261-1264).
IEEE DOI 1302
biomedical MRI BibRef

Jafarian, N.[Nassim], Kazemi, K.[Kamran], Abrishami Moghaddam, H.[Hamid], Grebe, R.[Reinhard], Fournier, M.[Marc], Helfroush, M.S.[Mohamad Sadegh], Gondry-Jouet, C.[Catherine], Wallois, F.[Fabrice],
Automatic segmentation of newborns' skull and fontanel from CT data using model-based variational level set,
SIViP(8), No. 2, February 2014, pp. 377-387.
Springer DOI 1402
BibRef

Bhadauria, H.S., Dewal, M.L.,
Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging,
SIViP(8), No. 2, February 2014, pp. 357-364.
WWW Link. 1402
BibRef

Ribbens, A., Hermans, J., Maes, F., Vandermeulen, D., Suetens, P.,
Unsupervised Segmentation, Clustering, and Groupwise Registration of Heterogeneous Populations of Brain MR Images,
MedImg(33), No. 2, February 2014, pp. 201-224.
IEEE DOI 1403
biomedical MRI BibRef

Kim, Y., Tagare, H.,
Intensity Nonuniformity Correction for Brain MR Images with Known Voxel Classes,
SIIMS(7), No. 1, 2014, pp. 528-557.
DOI Link 1404
BibRef

Martinez, M., Villagra, F., Loayza, F., Vidorreta, M., Arrondo, G., Luis, E., Diaz, J., Echeverria, M., Fernandez-Seara, M.A., Pastor, M.A.,
MRI-Compatible Device for Examining Brain Activation Related to Stepping,
MedImg(33), No. 5, May 2014, pp. 1044-1053.
IEEE DOI 1405
Blood oxygen measurements BibRef

Dong, F.F.[Fang-Fang], Peng, J.L.[Jia-Lin],
Brain MR image segmentation based on local Gaussian mixture model and nonlocal spatial regularization,
JVCIR(25), No. 5, 2014, pp. 827-839.
Elsevier DOI 1406
MR image BibRef

Moreno, J.C.[Juan C.], Prasath, V.B.S.[V.B. Surya], Proença, H.[Hugo], Palaniappan, K.,
Fast and globally convex multiphase active contours for brain MRI segmentation,
CVIU(125), No. 1, 2014, pp. 237-250.
Elsevier DOI 1406
Image segmentation BibRef

Kuncheva, L.I.[Ludmila I.], Martínez-Rego, D.[David], Yuen, K.S.L.[Kenneth S. L.], Linden, D.E.J.[David E. J.], Johnston, S.J.[Stephen J.],
A spatial discrepancy measure between voxel sets in brain imaging,
SIViP(8), No. 5, July 2014, pp. 913-922.
Springer DOI 1407
BibRef

Oguz, I., Sonka, M.,
LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain,
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Image reconstruction BibRef

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Discriminative Clustering and Feature Selection for Brain MRI Segmentation,
SPLetters(22), No. 5, May 2015, pp. 573-577.
IEEE DOI 1411
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biomedical MRI BibRef

Verma, H.[Hanuman], Agrawal, R.K.[Ramesh K.], Kumar, N.[Naveen],
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fuzzy entropy clustering BibRef

Choi, P.T.[Pui Tung], Lam, K.C.[Ka Chun], Lui, L.M.[Lok Ming],
FLASH: Fast Landmark Aligned Spherical Harmonic Parameterization for Genus-0 Closed Brain Surfaces,
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Spherical Conformal Parameterization of Genus-0 Point Clouds for Meshing,
SIIMS(9), No. 4, 2016, pp. 1582-1618.
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Hippocampal Shape Modeling Based on a Progressive Template Surface Deformation and its Verification,
MedImg(34), No. 6, June 2015, pp. 1242-1261.
IEEE DOI 1506
biomedical MRI BibRef

Zhang, Y.D.[Yu-Dong], Dong, Z.C.[Zheng-Chao], Ji, G.L.[Gen-Lin], Wang, S.H.[Shui-Hua],
Effect of spider-web-plot in MR brain image classification,
PRL(62), No. 1, 2015, pp. 14-16.
Elsevier DOI 1507
Magnetic resonance imaging BibRef

Rajalakshmi, N.[Natarajan], Prabha, V.L.[Viswanathan Lakshmi],
MRI brain image classification: a hybrid approach,
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magnetic resonance imaging (MRI) BibRef

Chung, M.K., Hanson, J.L., Ye, J., Davidson, R.J., Pollak, S.D.,
Persistent Homology in Sparse Regression and Its Application to Brain Morphometry,
MedImg(34), No. 9, September 2015, pp. 1928-1939.
IEEE DOI 1509
Brain modeling BibRef

Becker, H., Albera, L., Comon, P., Gribonval, R., Wendling, F., Merlet, I.,
Brain-Source Imaging: From sparse to tensor models,
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Biomedical signal processing BibRef

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Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine,
IJIST(25), No. 4, 2015, pp. 317-327.
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magnetic resonance imaging BibRef

Reiche, B.[Brittany], Moody, A.R.[Alan R.], Khademi, A.[April],
Effect of image standardization on FLAIR MRI for brain extraction,
SIViP(9), No. 1 Supp, December 2015, pp. 11-16.
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Li, J., Shi, Y., Toga, A.W.,
Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain,
SPMag(33), No. 3, May 2016, pp. 36-51.
IEEE DOI 1605
Biomedical image processing BibRef

Agnello, L.[Luca], Comelli, A.[Albert], Ardizzone, E.[Edoardo], Vitabile, S.[Salvatore],
Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis,
IJIST(26), No. 2, 2016, pp. 136-150.
DOI Link 1606
voxel-based morphometry BibRef

Chen, Y.J.[Yun-Jie], Zhang, H.[Hui], Zheng, Y.H.[Yu-Hui], Jeon, B.W.[Byeung-Woo], Wu, Q.M.J.[Q.M. Jonathan],
An improved anisotropic hierarchical fuzzy c-means method based on multivariate student t-distribution for brain MRI segmentation,
PR(60), No. 1, 2016, pp. 778-792.
Elsevier DOI 1609
Anisotropic spatial information BibRef

Bao, L., Li, X., Cai, C., Chen, Z., van Zijl, P.C.M.,
Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain,
MedImg(35), No. 9, September 2016, pp. 2040-2050.
IEEE DOI 1609
Collaboration BibRef

Marami, B., Scherrer, B., Afacan, O., Erem, B., Warfield, S.K., Gholipour, A.,
Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking,
MedImg(35), No. 10, October 2016, pp. 2258-2269.
IEEE DOI 1610
Image reconstruction BibRef

Zhang, J.P.[Jin-Peng], Zhang, L.C.[Li-Chi], Xiang, L.[Lei], Shao, Y.Q.[Ye-Qin], Wu, G.R.[Guo-Rong], Zhou, X.D.[Xiao-Dong], Shen, D.G.[Ding-Gang], Wang, Q.[Qian],
Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution,
PR(63), No. 1, 2017, pp. 531-541.
Elsevier DOI 1612
Brain atlas BibRef

Jeong, W.C., Sajib, S.Z.K., Katoch, N., Kim, H.J., Kwon, O.I., Woo, E.J.,
Anisotropic Conductivity Tensor Imaging of In Vivo Canine Brain Using DT-MREIT,
MedImg(36), No. 1, January 2017, pp. 124-131.
IEEE DOI 1701
Conductivity BibRef

Alchatzidis, S.[Stavros], Sotiras, A.[Aristeidis], Zacharaki, E.I.[Evangelia I.], Paragios, N.[Nikos],
A Discrete MRF Framework for Integrated Multi-Atlas Registration and Segmentation,
IJCV(121), No. 1, January 2017, pp. 169-181.
Springer DOI 1702
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Discrete Multi Atlas Segmentation using Agreement Constraints,
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Demirhan, A.[Ayse],
Neuroimage-based clinical prediction using machine learning tools,
IJIST(27), No. 1, 2017, pp. 89-97.
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structural brain MR images BibRef

Sophia, P.E.[P. Eben], Anitha, J.,
A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction,
IJIST(27), No. 2, 2017, pp. 171-181.
DOI Link 1706
contextual compression, contourlet transform, magnetic resonance imaging brain image, optimized prediction, wavelet, transform BibRef

Mohammadi-Nejad, A.R., Hossein-Zadeh, G.A., Soltanian-Zadeh, H.,
Structured and Sparse Canonical Correlation Analysis as a Brain-Wide Multi-Modal Data Fusion Approach,
MedImg(36), No. 7, July 2017, pp. 1438-1448.
IEEE DOI 1707
Correlation, Covariance matrices, Data integration, Magnetic resonance imaging, Neuroimaging, Standards, ADNI, canonical correlation analysis (CCA), magnetic resonance imaging (MRI), multi-modal data fusion, multivariate, analysis BibRef

Mohammadi-Nejad, A.R., Hossein-Zadeh, G.A., Soltanian-Zadeh, H.,
Multi-modal data fusion using group-structured sparse canonical correlation analysis: A simulation study,
IPRIA17(123-127)
IEEE DOI 1712
biomedical MRI, correlation methods, graph theory, image fusion, medical image processing, sparse matrices, visual databases, Overlapping and Disjoint Groups BibRef

Saladi, S.[Saritha], Prabha, N.A.[N. Amutha],
Analysis of denoising filters on MRI brain images,
IJIST(27), No. 3, 2017, pp. 201-208.
DOI Link 1708
bilateral filter, denoising, MRI brain, NLM, PCA, , SANLM BibRef

Osadebey, M.[Michael], Pedersen, M.[Marius], Arnold, D.[Douglas], Wendel-Mitoraj, K.[Katrina],
No-reference quality measure in brain MRI images using binary operations, texture and set analysis,
IET-IPR(11), No. 9, September 2017, pp. 672-684.
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Generalised rough intuitionistic fuzzy c-means for magnetic resonance brain image segmentation,
IET-IPR(11), No. 9, September 2017, pp. 777-785.
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Mohseni Salehi, S.S., Erdogmus, D., Gholipour, A.,
Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging,
MedImg(36), No. 11, November 2017, pp. 2319-2330.
IEEE DOI 1711
Context, Feature extraction, Image segmentation, Magnetic resonance imaging, BibRef

Gilanie, G.[Ghulam], Bajwa, U.I.[Usama Ijaz], Waraich, M.M.[Mustansar Mahmood], Habib, Z.[Zulfiqar], Ullah, H.[Hafeez], Nasir, M.[Muhammad],
Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines,
SIViP(12), No. 3, March 2018, pp. 479-487.
Springer DOI 1804
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Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.,
Multimodal MR Synthesis via Modality-Invariant Latent Representation,
MedImg(37), No. 3, March 2018, pp. 803-814.
IEEE DOI 1804
biomedical MRI, brain, convolution, feedforward neural nets, image representation, image segmentation, multi-modality fusion BibRef

Kahali, S.[Sayan], Adhikari, S.K.[Sudip Kumar], Sing, J.K.[Jamuna Kanta],
Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data,
IET-CV(12), No. 3, April 2018, pp. 288-297.
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Rodríguez-Domínguez, U.[Ulises], Dalmau, O.[Oscar], Bosch-Bayard, J.[Jorge],
Atlas-based segmentation of neonatal brain MR images using a gray matter enhancing step,
SIViP(12), No. 4, May 2018, pp. 633-640.
Springer DOI 1805
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El-Dahshan, E.S.A.[El-Sayed A.], Bassiouni, M.M.[Mahmoud M.],
Computational intelligence techniques for human brain MRI classification,
IJIST(28), No. 2, 2018, pp. 132-148.
WWW Link. 1806
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Choi, J., Bao, C., Zhang, X.,
PET-MRI Joint Reconstruction by Joint Sparsity Based Tight Frame Regularization,
SIIMS(11), No. 2, 2018, pp. 1179-1204.
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Liu, Y.L.[Yi-Lin], Du, H.Q.[Hui-Qian], Wang, Z.X.[Ze-Xian], Mei, W.B.[Wen-Bo],
Convex MR brain image reconstruction via non-convex total variation minimization,
IJIST(28), No. 4, December 2018, pp. 246-253.
WWW Link. 1811
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Hadj-Selem, F., Löfstedt, T., Dohmatob, E., Frouin, V., Dubois, M., Guillemot, V., Duchesnay, E.,
Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging,
MedImg(37), No. 11, November 2018, pp. 2403-2413.
IEEE DOI 1811
Smoothing methods, TV, Convergence, Magnetic resonance imaging, Neuroimaging, Brain, Neuroimaging, convex optimization BibRef

Qasim, A.F.[Asaad F.], Aspin, R.[Rob], Meziane, F.[Farid], Hogg, P.[Peter],
Assessment of perceptual distortion boundary through applying reversible watermarking to brain MR images,
SP:IC(70), 2019, pp. 246-258.
Elsevier DOI 1812
Medical imaging, DICOM, Reversible watermarking, Imperceptibility, Image quality, Visual grading analysis BibRef

van Opbroek, A., Achterberg, H.C., Vernooij, M.W., de Bruijne, M.,
Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning,
MedImg(38), No. 1, January 2019, pp. 213-224.
IEEE DOI 1901
Training, Kernel, Image segmentation, Biomedical imaging, Probability density function, Learning systems, Brain, magnetic resonance imaging BibRef

Zhang, Y., Shi, F., Cheng, J., Wang, L., Yap, P., Shen, D.,
Longitudinally Guided Super-Resolution of Neonatal Brain Magnetic Resonance Images,
Cyber(49), No. 2, February 2019, pp. 662-674.
IEEE DOI 1901
Pediatrics, Image reconstruction, Spatial resolution, Brain, Guided bilateral filtering (GBF), image interpolation, total variation (TV) BibRef

Saboori, A.[Arash], Birjandtalab, J.[Javad],
PET-MRI image fusion using adaptive filter based on spectral and spatial discrepancy,
SIViP(13), No. 1, February 2019, pp. 135-143.
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Balachandrasekaran, A., Mani, M., Jacob, M.,
Calibration-Free B0 Correction of EPI Data Using Structured Low Rank Matrix Recovery,
MedImg(38), No. 4, April 2019, pp. 979-990.
IEEE DOI 1904
Nonhomogeneous media, Distortion, Transmission line matrix methods, Magnetic resonance imaging, annihilation filter BibRef

Wei, J.[Jie], Xia, Y.[Yong], Zhang, Y.[Yanning],
M3Net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation,
PR(91), 2019, pp. 366-378.
Elsevier DOI 1904
Brain image segmentation, Deep learning, U-Net, Convolutional auto-encoder, Back propagation neural network, Magnetic resonance imaging image BibRef

Bao, C.L.[Cheng-Long], Choi, J.K.[Jae Kyu], Dong, B.[Bin],
Whole Brain Susceptibility Mapping Using Harmonic Incompatibility Removal,
SIIMS(12), No. 1, 2019, pp. 492-520.
DOI Link 1904
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Liang, H., Dabrowska, N., Kapur, J., Weller, D.S.,
Structure-Based Intensity Propagation for 3-D Brain Reconstruction With Multilayer Section Microscopy,
MedImg(38), No. 5, May 2019, pp. 1106-1115.
IEEE DOI 1905
Slices. Image reconstruction, Nonhomogeneous media, Brain, Microscopy, Image resolution, registration BibRef

Katoch, N., Choi, B.K., Sajib, S.Z.K., Lee, E., Kim, H.J., Kwon, O.I., Woo, E.J.,
Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension,
MedImg(38), No. 7, July 2019, pp. 1569-1577.
IEEE DOI 1907
Conductivity, Magnetic resonance imaging, Electrolytes, Extracellular, In vivo, Conductivity tensor imaging (CTI), magnetic resonance imaging BibRef

Gilanie, G.[Ghulam], Bajwa, U.I.[Usama Ijaz], Waraich, M.M.[Mustansar Mahmood], Habib, Z.[Zulfiqar],
Computer aided diagnosis of brain abnormalities using texture analysis of MRI images,
IJIST(29), No. 3, September 2019, pp. 260-271.
DOI Link 1908
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Yoo, C.H.[Chang Hyun], Oh, J.[Janghoon], Park, S.[Soonchan], Ryu, C.W.[Chang-Woo], Kwon, Y.K.[Young Kyun], Jahng, G.H.[Geon-Ho],
Comparative evaluation of the polynomial and spline fitting methods for the B0 correction of CEST MRI data acquired from human brains,
IJIST(29), No. 3, September 2019, pp. 272-282.
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Roy, S.[Shaswati], Maji, P.[Pradipta],
Rough segmentation of coherent local intensity for bias induced 3-D MR brain images,
PR(97), 2020, pp. 106997.
Elsevier DOI 1910
Magnetic resonance image, Segmentation, Intensity inhomogeneity, Rough sets, Coherent local intensity clustering BibRef

Cherukuri, V.[Venkateswararao], Guo, T.T.[Tian-Tong], Schiff, S.J.[Steven J.], Monga, V.[Vishal],
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors,
IP(29), No. , 2020, pp. 1368-1383.
IEEE DOI 1911
BibRef
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Deep MR Image Super-Resolution Using Structural Priors,
ICIP18(410-414)
IEEE DOI 1809
Laplace equations, Deep learning, Training, Spatial resolution, Interpolation, MR, deep learning, priors, low-rank. Training, Machine learning, Databases, Brain, Super Resolution, MR Image Processing BibRef

Debakla, M.[Mohammed], Salem, M.[Mohamed], Djemal, K.[Khalifa], Benmeriem, K.[Khaled],
Fuzzy farthest point first method for MRI brain image clustering,
IET-IPR(13), No. 13, November 2019, pp. 2395-2400.
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Park, J.A., Kang, K.J., Ko, I.O., Lee, K.C., Choi, B.K., Katoch, N., Kim, J.W., Kim, H.J., Kwon, O.I., Woo, E.J.,
In Vivo Measurement of Brain Tissue Response After Irradiation: Comparison of T2 Relaxation, Apparent Diffusion Coefficient, and Electrical Conductivity,
MedImg(38), No. 12, December 2019, pp. 2779-2784.
IEEE DOI 1912
Conductivity, Radiation effects, Mice, Magnetic resonance imaging, In vivo, Conductivity measurement, Radiation therapy, tissue response BibRef

Ma, B., Gaens, M., Caldeira, L., Bert, J., Lohmann, P., Tellmann, L., Lerche, C., Scheins, J., Kops, E.R.[E. Rota], Xu, H., Lenz, M., Pietrzyk, U., Shah, N.J.,
Scatter Correction Based on GPU-Accelerated Full Monte Carlo Simulation for Brain PET/MRI,
MedImg(39), No. 1, January 2020, pp. 140-151.
IEEE DOI 2001
Photonics, Graphics processing units, Phantoms, Detectors, Monte Carlo methods, Brain modeling, GPU, Monte Carlo simulation, single scatter simulation BibRef

Chung, K.J., Souza, R., Frayne, R.,
Restoration of Lossy JPEG-Compressed Brain MR Images Using Cross-Domain Neural Networks,
SPLetters(27), 2020, pp. 141-145.
IEEE DOI 2001
Convolutional neural network (CNN), image reconstruction, JPEG decompression, teleradiography BibRef

Zamani, H., Razavikia, S., Otroshi-Shahreza, H., Amini, A.,
Separation of Nonlinearly Mixed Sources Using End-to-End Deep Neural Networks,
SPLetters(27), 2020, pp. 101-105.
IEEE DOI 2001
Training, Taylor series, Recurrent neural networks, Brain modeling, Blind source separation, Blindness BibRef

Sun, L., Ma, W., Ding, X., Huang, Y., Liang, D., Paisley, J.,
A 3D Spatially Weighted Network for Segmentation of Brain Tissue From MRI,
MedImg(39), No. 4, April 2020, pp. 898-909.
IEEE DOI 2004
Magnetic resonance imaging, Image segmentation, Brain modeling, Hidden Markov models, multimodality MRI BibRef

Sun, L., Shao, W., Zhang, D., Liu, M.,
Anatomical Attention Guided Deep Networks for ROI Segmentation of Brain MR Images,
MedImg(39), No. 6, June 2020, pp. 2000-2012.
IEEE DOI 2006
Anatomical attention, deep learning, ROI segmentation, brain MR image BibRef

Liu, M., Zhang, J., Lian, C., Shen, D.,
Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores,
Cyber(50), No. 7, July 2020, pp. 3381-3392.
IEEE DOI 2006
Magnetic resonance imaging, Diseases, Brain modeling, Feature extraction, Training, Deep learning, weakly supervised learning BibRef

Mishro, P.K.[Pranaba K.], Agrawal, S.[Sanjay], Panda, R.[Rutuparna], Abraham, A.[Ajith],
Novel fuzzy clustering-based bias field correction technique for brain magnetic resonance images,
IET-IPR(14), No. 9, 20 July 2020, pp. 1929-1936.
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Segmentation of MR Brain Images Through Hidden Markov Random Field and Hybrid Metaheuristic Algorithm,
IP(29), 2020, pp. 6507-6522.
IEEE DOI 2007
Image segmentation, Hidden Markov models, Optimization, Adaptation models, Brain, Particle swarm optimization, particle swarm optimization BibRef

Liu, H.[Hong], Xu, L.J.[Li-Jun], Song, E.[Enmin], Jin, R.C.[Ren-Chao], Hung, C.C.[Chih-Cheng],
Automatic labelling of brain tissues in MR images through spatial indexes based hybrid atlas forest,
IET-IPR(14), No. 12, October 2020, pp. 2728-2736.
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Nayak, D.R.[Deepak Ranjan], Dash, R.[Ratnakar], Majhi, B.[Banshidhar],
Automated diagnosis of multi-class brain abnormalities using MRI images: A deep convolutional neural network based method,
PRL(138), 2020, pp. 385-391.
Elsevier DOI 1806
Magnetic resonance imaging, Deep learning, Brain disease, CNN, Transfer learning BibRef

Lu, S.Y.[Si-Yuan], Wang, S.H.[Shui-Hua], Zhang, Y.D.[Yu-Dong],
A classification method for brain MRI via MobileNet and feedforward network with random weights,
PRL(140), 2020, pp. 252-260.
Elsevier DOI 2012
Computer aided diagnosis, Magnetic resonance image, MobileNet, Extreme learning machine, Random vector functional-link net, Visual question answering BibRef

Jiao, J., Namburete, A.I.L., Papageorghiou, A.T., Noble, J.A.,
Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis,
MedImg(39), No. 12, December 2020, pp. 4413-4424.
IEEE DOI 2012
Magnetic resonance imaging, Computed tomography, Image synthesis, Image segmentation, Biomedical imaging, MRI BibRef

Dou, H., Karimi, D., Rollins, C.K., Ortinau, C.M., Vasung, L., Velasco-Annis, C., Ouaalam, A., Yang, X., Ni, D., Gholipour, A.,
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI,
MedImg(40), No. 4, April 2021, pp. 1123-1133.
IEEE DOI 2104
Magnetic resonance imaging, Image segmentation, Image reconstruction, Brain, attention BibRef

Li, Y.H.[Ya-Hang], Wang, Z.P.[Ze-Peng], Sun, R.Y.[Ruo-Yu], Lam, F.[Fan],
Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations,
MedImg(40), No. 4, April 2021, pp. 1157-1167.
IEEE DOI 2104
Short-echo-time (TE) proton magnetic resonance spectroscopic imaging (MRSI). Data models, Training, Source separation, Imaging, Artificial neural networks, Numerical models, Manifolds, low-dimensional models BibRef

Li, B.[Bicao], Liu, Z.F.[Zhou-Feng], Gao, S.[Shan], Hwang, J.N.[Jenq-Neng], Sun, J.[Jun], Wang, Z.M.[Zong-Min],
CSpA-DN: Channel and Spatial Attention Dense Network for Fusing PET and MRI Images,
ICPR21(8188-8195)
IEEE DOI 2105
Measurement, Magnetic resonance imaging, Neural networks, Feature extraction, Decoding, Pattern recognition, Image fusion, PET and MRI BibRef

Li, Z.[Zeju], Yu, J.H.[Jin-Hu], Wang, Y.Y.[Yuan-Yuan], Zhou, H.Z.[Han-Zhang], Yang, H.W.[Hao-Wei], -, Qiao, Z.W.[Zhong-Wei],
DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution,
Cyber(51), No. 7, July 2021, pp. 3441-3454.
IEEE DOI 2106
Magnetic resonance imaging, Brain, Image reconstruction, Volume measurement, Biomedical imaging, Brain volume estimation, thick-section MRI BibRef

Armanious, K.[Karim], Abdulatif, S.[Sherif], Shi, W.B.[Wen-Bin], Salian, S.[Shashank], Küstner, T.[Thomas], Weiskopf, D.[Daniel], Hepp, T.[Tobias], Gatidis, S.[Sergios], Yang, B.[Bin],
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation,
MedImg(40), No. 7, July 2021, pp. 1778-1791.
IEEE DOI 2107
Estimation, Aging, Biological systems, Magnetic resonance imaging, Biomedical imaging, Genetics, magnetic resonance imaging BibRef

Hoffmann, M.[Malte], Turk, E.A.[Esra Abaci], Gagoski, B.[Borjan], Morgan, L.[Leah], Wighton, P.[Paul], Tisdall, M.D.[Matthew Dylan], Reuter, M.[Martin], Adalsteinsson, E.[Elfar], Grant, P.E.[Patricia Ellen], Wald, L.L.[Lawrence L.], van der Kouwe, A.J.W.[André J. W.],
Rapid head-pose detection for automated slice prescription of fetal-brain MRI,
IJIST(31), No. 3, 2021, pp. 1136-1154.
DOI Link 2108
fetal MRI, head-pose detection, MSER, scan automation, scan prescription, slice positioning BibRef

Wu, H.S.[Hui-Si], Chen, X.J.[Xiu-Juan], Li, P.[Ping], Wen, Z.K.[Zhen-Kun],
Automatic Symmetry Detection From Brain MRI Based on a 2-Channel Convolutional Neural Network,
Cyber(51), No. 9, September 2021, pp. 4464-4475.
IEEE DOI 2109
Brain, Feature extraction, Biomedical imaging, Image segmentation, Image analysis, Magnetic resonance imaging, symmetry detection BibRef

Cheng, J.[Jian], Liu, Z.Y.[Zi-Yang], Guan, H.[Hao], Wu, Z.Z.[Zhen-Zhou], Zhu, H.G.[Hao-Gang], Jiang, J.[Jiyang], Wen, W.[Wei], Tao, D.C.[Da-Cheng], Liu, T.[Tao],
Brain Age Estimation From MRI Using Cascade Networks With Ranking Loss,
MedImg(40), No. 12, December 2021, pp. 3400-3412.
IEEE DOI 2112
Estimation, Magnetic resonance imaging, Support vector machines, Brain modeling, Biomedical imaging, ranking loss BibRef

Li, C.[Chao], Sun, J.[Jun], Liu, L.[Li], Palade, V.[Vasile],
A hybrid framework for brain tissue segmentation in magnetic resonance images,
IJIST(31), No. 4, 2021, pp. 2305-2321.
DOI Link 2112
brain tissue segmentation, expectation-maximization, hidden Markov random field, magnetic resonance image, random drift particle swarm optimization BibRef

He, S.[Sheng], Grant, P.E.[P. Ellen], Ou, Y.M.[Yang-Ming],
Global-Local Transformer for Brain Age Estimation,
MedImg(41), No. 1, January 2022, pp. 213-224.
IEEE DOI 2201
Estimation, Magnetic resonance imaging, Feature extraction, Biological neural networks, Fuses, Data mining, interpretation BibRef

Ravikumar, M., Shivaprasad, B.J., Guru, D.S.,
Enhancement of MRI Brain Images Using Notch Filter Based on Discrete Wavelet Transform,
IJIG(22), No. 1 2022, pp. 2250010.
DOI Link 2202
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Chaoui, C.N.[Cherifa Nabila], Ghomari, A.[Abdelghani], Meftah, B.[Boudjelal],
Edge and anomaly detection of brain magnetic resonance images in a distributed environment,
IJIST(32), No. 2, 2022, pp. 642-657.
DOI Link 2203
brain MRI image, edge detection, Hodgkin and Huxley model, interpretation, multi-agent systems, segmentation, visual cortex BibRef

Wei, J.[Jie], Wu, Z.W.[Zheng-Wang], Wang, L.[Li], Bui, T.D.[Toan Duc], Qu, L.Q.[Liang-Qiong], Yap, P.T.[Pew-Thian], Xia, Y.[Yong], Li, G.[Gang], Shen, D.G.[Ding-Gang],
A cascaded nested network for 3T brain MR image segmentation guided by 7T labeling,
PR(124), 2022, pp. 108420.
Elsevier DOI 2203
Brain segmentation, Cascaded nested network, Deep learning, Magnetic resonance imaging BibRef

Cheng, N.[Ning], Cao, C.Z.[Chun-Zheng], Yang, J.W.[Jian-Wei], Zhang, Z.C.[Zhi-Chao], Chen, Y.J.[Yun-Jie],
A spatially constrained skew Student's-t mixture model for brain MR image segmentation and bias field correction,
PR(128), 2022, pp. 108658.
Elsevier DOI 2205
Bias field, EM Algorithm, Skew student's-t distribution, Two-level spatial information BibRef

Wei, D.M.[Dong-Ming], Ahmad, S.[Sahar], Guo, Y.Y.[Yu-Yu], Chen, L.Y.[Li-Yun], Huang, Y.Z.[Yun-Zhi], Ma, L.[Lei], Wu, Z.W.[Zheng-Wang], Li, G.[Gang], Wang, L.[Li], Lin, W.[Weili], Yap, P.T.[Pew-Thian], Shen, D.G.[Ding-Gang], Wang, Q.[Qian],
Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images,
MedImg(41), No. 5, May 2022, pp. 1219-1229.
IEEE DOI 2205
Strain, Training, Image segmentation, Brain, Image registration, National Institutes of Health, Deep learning, tissue-aware regularization BibRef

di Ianni, T.[Tommaso], Airan, R.D.[Raag D.],
Deep-fUS: A Deep Learning Platform for Functional Ultrasound Imaging of the Brain Using Sparse Data,
MedImg(41), No. 7, July 2022, pp. 1813-1825.
IEEE DOI 2207
Doppler effect, Ultrasonic imaging, Compounds, Imaging, Convolution, Image reconstruction, Clutter, Functional ultrasound imaging, Doppler ultrasound BibRef

Naik, M.K.[Manoj Kumar], Panda, R.[Rutuparna], Samantaray, L.[Leena], Abraham, A.[Ajith],
A novel threshold score based multiclass segmentation technique for brain magnetic resonance images using adaptive opposition slime mold algorithm,
IJIST(32), No. 4, 2022, pp. 1397-1413.
DOI Link 2207
brain MR image, evolutionary computing, multilevel thresholding, slime mold algorithm BibRef

Xia, J.[Jing], Sha, J.T.[Jin-Tao], Zhang, Q.[Qian],
Robust 3D brain segmentation in magnetic resonance image with weighted feature fusion,
IET-IPR(16), No. 11, 2022, pp. 3000-3010.
DOI Link 2208
BibRef

Sarabian, M.[Mohammad], Babaee, H.[Hessam], Laksari, K.[Kaveh],
Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging,
MedImg(41), No. 9, September 2022, pp. 2285-2303.
IEEE DOI 2209
Magnetic resonance imaging, Hemodynamics, Computational modeling, Brain modeling, Blood, Velocity measurement, Arteries, 4D flow MRI BibRef

He, S.[Sheng], Feng, Y.F.[Yan-Fang], Grant, P.E.[P. Ellen], Ou, Y.M.[Yang-Ming],
Deep Relation Learning for Regression and Its Application to Brain Age Estimation,
MedImg(41), No. 9, September 2022, pp. 2304-2317.
IEEE DOI 2209
Feature extraction, Estimation, Magnetic resonance imaging, Brain modeling, Transformers, Biological neural networks, Tensors, deep learning BibRef

Lv, J.X.[Jin-Xin], Wang, Z.W.[Zhi-Wei], Shi, H.[Hongkuan], Zhang, H.[Haobo], Wang, S.[Sheng], Wang, Y.[Yilang], Li, Q.[Qiang],
Joint Progressive and Coarse-to-Fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion,
MedImg(41), No. 10, October 2022, pp. 2788-2802.
IEEE DOI 2210
Strain, Magnetic resonance imaging, Decoding, Training, Estimation, Deformable models, Learning systems, subcortical nuclei BibRef

Yang, Q.Q.[Qin-Qin], Lin, Y.H.[Yan-Hong], Wang, J.C.[Jie-Chao], Bao, J.F.[Jian-Feng], Wang, X.Y.[Xiao-Yin], Ma, L.C.[Ling-Ceng], Zhou, Z.[Zihan], Yang, Q.Z.[Qi-Zhi], Cai, S.H.[Shu-Hui], He, H.J.[Hong-Jian], Cai, C.B.[Cong-Bo], Dong, J.Y.[Ji-Yang], Cheng, J.L.[Jing-Liang], Chen, Z.[Zhong], Zhong, J.H.[Jian-Hui],
MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T2 Mapping With Severe Head Motion Using Overlapping-Echo Acquisition,
MedImg(41), No. 11, November 2022, pp. 3167-3181.
IEEE DOI 2211
Data models, Magnetic resonance imaging, Imaging, Image reconstruction, Training, Deep learning, Optimization, calibrationless parallel reconstruction BibRef

Shi, W.[Wen], Xu, H.[Haoan], Sun, C.[Cong], Sun, J.W.[Ji-Wei], Li, Y.M.[Ya-Min], Xu, X.[Xinyi], Zheng, T.S.[Tian-Shu], Zhang, Y.[Yi], Wang, G.B.[Guang-Bin], Wu, D.[Dan],
AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI,
MedImg(42), No. 1, January 2023, pp. 209-219.
IEEE DOI 2301
Image reconstruction, Magnetic resonance imaging, Motion estimation, Feature extraction, Pipelines, Superresolution, super-resolution reconstruction BibRef

Ma, Q.[Qiang], Li, L.[Liu], Robinson, E.C.[Emma C.], Kainz, B.[Bernhard], Rueckert, D.[Daniel], Alansary, A.[Amir],
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs,
MedImg(42), No. 2, February 2023, pp. 430-443.
IEEE DOI 2302
Surface reconstruction, Surface treatment, Pipelines, Surface morphology, Image reconstruction, neural ODE BibRef

Cordero-Grande, L.[Lucilio], Ortuño-Fisac, J.E.[Juan Enrique], del Hoyo, A.A.[Alejandra Aguado], Uus, A.[Alena], Deprez, M.[Maria], Santos, A.[Andres], Hajnal, J.V.[Joseph V.], Ledesma-Carbayo, M.J.[María J.],
Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration,
MedImg(42), No. 3, March 2023, pp. 810-822.
IEEE DOI 2303
Image reconstruction, Strain, Magnetic resonance imaging, Estimation, Brain modeling, Reconstruction algorithms, gestational age prediction BibRef

Yu, Z.Q.[Zi-Qi], Han, X.Y.[Xiao-Yang], Zhang, S.J.[Sheng-Jie], Feng, J.F.[Jian-Feng], Peng, T.Y.[Ting-Ying], Zhang, X.Y.[Xiao-Yong],
MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain,
MedImg(42), No. 4, April 2023, pp. 1197-1209.
IEEE DOI 2304
Image segmentation, Mice, Magnetic resonance imaging, Task analysis, Training, Periodic structures, Brain modeling, disentangled representations BibRef

Wang, H.Z.[Han-Zhi], Treder, M.S.[Matthias S.], Marshall, D.[David], Jones, D.K.[Derek K.], Li, Y.H.[Yu-Hua],
A Skewed Loss Function for Correcting Predictive Bias in Brain Age Prediction,
MedImg(42), No. 6, June 2023, pp. 1577-1589.
IEEE DOI 2306
Brain modeling, Predictive models, Training, Mathematical models, Estimation, Deep learning, Brain age delta, deep learning, regression bias correction BibRef

Xu, J.[Junshen], Moyer, D.[Daniel], Gagoski, B.[Borjan], Iglesias, J.E.[Juan Eugenio], Grant, P.E.[P. Ellen], Golland, P.[Polina], Adalsteinsson, E.[Elfar],
NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI,
MedImg(42), No. 6, June 2023, pp. 1707-1719.
IEEE DOI 2306
Image reconstruction, Solid modeling, Magnetic resonance imaging, Encoding, Training, Biomedical imaging, MRI, fetal brain MRI BibRef

Galarce, F.[Felipe], Tabelow, K.[Karsten], Polzehl, J.[Jorg], Papanikas, C.P.[Christos Panagiotis], Vavourakis, V.[Vasileios], Lilaj, L.[Ledia], Sack, I.[Ingolf], Caiazzo, A.[Alfonso],
Displacement and Pressure Reconstruction from Magnetic Resonance Elastography Images: Application to an In Silico Brain Model,
SIIMS(16), No. 2, 2023, pp. 996-1027.
DOI Link 2306
BibRef

Heredia-Lidón, Á.[Álvaro], González, A.[Alejandro], Guerrero-Mosquera, C.[Carlos], Gonzàlez-Colom, R.[Rubèn], Echeverry, L.M.[Luis M.], Hostalet, N.[Noemí], Salvador, R.[Raymond], Pomarol-Clotet, E.[Edith], Fortea, J.[Juan], Martínez-Abadías, N.[Neus], Fatjó-Vilas, M.[Mar], Sevillano, X.[Xavier],
Automated Orientation Detection of 3d Head Reconstructions from SMRI Using Multiview Orthographic Projections: An Image Classification-based Approach,
IbPRIA23(603-614).
Springer DOI 2307
BibRef

Woo, M.K.[Myung Kyun], DelaBarre, L.[Lance], Waks, M.[Matt], Lagore, R.[Russell], Kim, J.[Jeehoon], Jungst, S.[Steve], Eryaman, Y.[Yigitcan], Ugurbil, K.[Kamil], Adriany, G.[Gregor],
A 32-Channel Sleeve Antenna Receiver Array for Human Head MRI Applications at 10.5 T,
MedImg(42), No. 9, September 2023, pp. 2643-2652.
IEEE DOI 2310
BibRef

Huang, Y.Z.[Yun-Zhi], Ahmad, S.[Sahar], Han, L.[Luyi], Wang, S.[Shuai], Wu, Z.[Zhengwang], Lin, W.[Weili], Li, G.[Gang], Wang, L.[Li], Yap, P.T.[Pew-Thian],
Longitudinal prediction of postnatal brain magnetic resonance images via a metamorphic generative adversarial network,
PR(143), 2023, pp. 109715.
Elsevier DOI 2310
Infant brain MRI, Longitudinal prediction, Metamorphic GAN BibRef

Krishnasamy, N.[Narayanan], Ponnusamy, T.[Thangaraj],
Deep learning-based robust hybrid approaches for brain tumor classification in magnetic resonance images,
IJIST(33), No. 6, 2023, pp. 2157-2177.
DOI Link 2311
brain tumor classification, deep learning, fully convolutional networks, magnetic resonance imaging, residual networks threshold-based segmentation BibRef

Huang, T.M.[Tsung-Ming], Liao, W.H.[Wei-Hung], Lin, W.W.[Wen-Wei], Yueh, M.H.[Mei-Heng], Yau, S.T.[Shing-Tung],
Convergence Analysis of Volumetric Stretch Energy Minimization and Its Associated Optimal Mass Transport,
SIIMS(16), No. 3, 2023, pp. 1825-1855.
DOI Link 2312
BibRef

Sa, B.K.[Bijay Kumar], Panda, R.[Rutuparna], Agrawal, S.[Sanjay],
Relevant edge probability-based adaptively weighted active contour for medical image segmentation,
IJIST(34), No. 2, 2024, pp. e22993.
DOI Link 2402
active contour, biomedical images, brain MR image, level set, segmentation, ultrasound image BibRef

Shyna, A., Amma, C.U.[C. Ushadevi], John, A.[Ansamma], Kesavadas, C., Thomas, B.[Bejoy],
Dual independent pathway-densely connected residual network with dilated convolution-based arterial spin labeling MRI image reconstruction with minimum label-control pairs,
IJIST(34), No. 2, 2024, pp. e23040.
DOI Link 2402
arterial spin labeling, cerebral blood flow, densely connected residual network, dilated convolution, signal-to-noise ratio BibRef

Chow, L.S.[Li Sze], Paley, M.N.J.[Martyn N. J.], Hickman, S.J.[Simon J.],
Evaluation of optimal interpolation and segmentation of the optic nerves on magnetic resonance images for cross-sectional area measurement,
IJIST(34), No. 2, 2024, pp. e23030.
DOI Link 2402
interpolation, Lanczos, mFCM, optic nerve, segmentation BibRef

Moazami, S.[Saeed], Ray, D.[Deep], Pelletier, D.[Daniel], Oberai, A.A.[Assad A.],
Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks,
MedImg(43), No. 3, March 2024, pp. 1071-1088.
IEEE DOI Code:
WWW Link. 2403
Biomedical imaging, Task analysis, Brain modeling, Image segmentation, Head, Brain, Uncertainty, Bayesian inference, uncertainty quantification BibRef

Pei, Y.C.[Yu-Chen], Zhao, F.[Fenqiang], Zhong, T.[Tao], Ma, L.[Laifa], Liao, L.[Lufan], Wu, Z.[Zhengwang], Wang, L.[Li], Zhang, H.[He], Wang, L.S.[Li-Sheng], Li, G.[Gang],
PETS-Nets: Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks,
MedImg(43), No. 3, March 2024, pp. 1006-1017.
IEEE DOI 2403
Image segmentation, Pose estimation, Motion segmentation, Magnetic resonance imaging, Task analysis, Image reconstruction, tissue segmentation BibRef

Wei, S.[Shuning], Chen, H.J.[Hai-Jun], Zhao, J.Q.[Jun-Qi], Su, F.Y.[Feng-Yi], Peng, S.L.[Shin-Lei],
Range and variability of CBF in young adults: PC-MRI and ASL studies,
IJIST(34), No. 2, 2024, pp. e22986.
DOI Link 2402
arterial spin labeling. flow, intersubject variation, sex, velocity BibRef

Fu, M.H.[Ming-Han], Zhang, N.[Na], Huang, Z.X.[Zhen-Xing], Zhou, C.[Chao], Zhang, X.[Xu], Yuan, J.M.[Jian-Min], He, Q.[Qiang], Yang, Y.F.[Yong-Feng], Zheng, H.R.[Hai-Rong], Liang, D.[Dong], Wu, F.X.[Fang-Xiang], Fan, W.[Wei], Hu, Z.L.[Zhan-Li],
OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising,
MedImg(43), No. 4, April 2024, pp. 1554-1567.
IEEE DOI 2404
Noise reduction, Feature extraction, Imaging, Positron emission tomography, Image denoising, Optical flow, deep learning BibRef

Jiang, S.F.[Shao-Feng], Chen, X.Y.[Xing-Yan], Yi, C.[Chen],
SSA-UNet: Whole brain segmentation by U-Net with squeeze-and-excitation block and self-attention block from the 2.5D slice image,
IET-IPR(18), No. 6, 2024, pp. 1598-1612.
DOI Link 2405
biomedical imaging, biomedical MRI, convolutional neural nets BibRef

Meng, X.X.[Xiang-Xi], Sun, K.[Kaicong], Xu, J.[Jun], He, X.M.[Xu-Ming], Shen, D.G.[Ding-Gang],
Multi-Modal Modality-Masked Diffusion Network for Brain MRI Synthesis With Random Modality Missing,
MedImg(43), No. 7, July 2024, pp. 2587-2598.
IEEE DOI 2407
Magnetic resonance imaging, Noise reduction, Brain modeling, Task analysis, Predictive models, Image synthesis, Gaussian noise, multi-modal multi-task learning BibRef

Omidi, A.[Abbas], Mohammadshahi, A.[Aida], Gianchandani, N.[Neha], King, R.[Regan], Leijser, L.[Lara], Souza, R.[Roberto],
Unsupervised Domain Adaptation of MRI Skull-stripping Trained on Adult Data to Newborns,
WACV24(7703-7712)
IEEE DOI Code:
WWW Link. 2404
Training, Image segmentation, Pediatrics, Adaptation models, Magnetic resonance imaging, Brain modeling, Data models, Image recognition and understanding BibRef

Tzardis, V.[Vangelis], Loizou, C.P.[Christos P.], Kyriacou, E.[Efthyvoulos],
Semi-automated Lesions Segmentation of Brain Metastases in MRI Images,
CAIP23(I:216-226).
Springer DOI 2312
BibRef