21.11.2 Segmentation, Features, Models from Magnetic Resonance Data, MRI

Haralick, R.M., Nelson, A., Kim, Y., Anderson, P., Johnson, R., and DeSoto, L.,
Stereo and Multiplanar Video Display of 3-D Magnetic Resonance Image Data,
), JIT(15), No. 2, April, 1989, pp. 74-78. BibRef 8904

Snyder, W.E., Logenthiran, A., Santago, P., Link, K., Bilbro, G.L., Rajala, S.,
Segmentation of magnetic resonance images using mean field annealing,
IVC(10), No. 6, July-August 1992, pp. 361-368.
Elsevier DOI 0401
BibRef

Han, Y.S.[Youn-Sik], Snyder, W.E.[Wesley E.], Bilbro, G.L.[Griff L.],
Discontinuity-Preserving Vector Smoothing of Multivariate MR Images Using Vector Mean Field Annealing,
JMIV(9), No. 3, November 1998, pp. 199-212.
DOI Link BibRef 9811

Bezdek, J.C., Hall, L.O., Clarke, L.P.,
Review of MR image segmentation techniques using pattern recognition,
MedPhys(20), No. 4, July/August 1993, pp. 1033-1048. BibRef 9307

Toulson, D.L., Boyce, J.F.,
Segmentation of MR images using neural nets,
IVC(10), No. 5, June 1992, pp. 324-328.
Elsevier DOI 0401
BibRef
Earlier: BMVC91(xx-yy).
PDF File. 9109
BibRef

de Munck, J.C., Bhagwandien, R., Muller, S.H., Verster, F.C., van Herk, M.B.,
The computation of MR image distortions caused by tissue susceptibility using the boundary element method,
MedImg(15), No. 5, October 1996, pp. 620-627.
IEEE Top Reference. 0203
BibRef

Nosratinia, A., Mohsenian, N., Orchard, M.T., Liu, B.,
Interframe coding of magnetic resonance images,
MedImg(15), No. 5, October 1996, pp. 639-647.
IEEE Top Reference. 0203
BibRef
Earlier: A1, A3, A2, A4:
Interslice coding of magnetic resonance images using deformable triangular patches,
ICIP94(II: 898-902).
IEEE DOI 9411
BibRef

Orchard, M.T., Nosratinia, A., Rajagopalan, R.,
On interframe coding models for volumetric medical data,
ICIP95(II: 17-20).
IEEE DOI 9510
BibRef

Laidlaw, D.H., Fleischer, K.W., Barr, A.H.,
Partial Volume Bayesian Classification of Material Mixtures in MR Volume Data Using Voxel Histograms,
MedImg(17), No. 1, February 1998, pp. 74-86.
IEEE Top Reference. 9806
BibRef

Lelieveldt, B.P.F.[Boudewijn P. F.], Sonka, M.[Milan], Bolinger, L.[Lizann], Scholz, T.D.[Thomas D.], Kayser, H.[Hein], van der Geest, R.[Rob], Reiber, J.H.C.[Johan H. C.],
Anatomical Modeling with Fuzzy Implicit Surface Templates: Application to Automated Localization of the Heart and Lungs in Thoracic MR Volumes,
CVIU(80), No. 1, October 2000, pp. 1-20.
DOI Link 0010
BibRef

Styner, M., Brechbuhler, C., Szckely, G., Gerig, G.,
Parametric estimate of intensity inhomogeneities applied to MRI,
MedImg(19), No. 3, March 2000, pp. 153-165.
IEEE Top Reference. 0110
BibRef

Wilson, D.L., Noble, J.A.,
An adaptive segmentation algorithm for time-of-flight MRA data,
MedImg(18), No. 10, October 1999, pp. 938-945.
IEEE Top Reference. 0110
BibRef

Gao, Y.[Yun], Reeves, S.J.,
Fast k-space sample selection in MRSI with a limited region of support,
MedImg(20), No. 9, September 2001, pp. 868-876.
IEEE Top Reference. 0110
BibRef

van der Weide, R., Bakker, C.J., Viergever, M.A.,
Localization of intravascular devices with paramagnetic markers in MR images,
MedImg(20), No. 10, October 2001, pp. 1061-1071.
IEEE Top Reference. 0111
BibRef

Ashton, E.A., Parker, K.J., Berg, M.J., Chen, C.W.[Chang Wen],
A novel volumetric feature extraction technique with applications to MR images,
MedImg(16), No. 4, August 1997, pp. 365-371.
IEEE Top Reference. 0205
BibRef
Earlier: ICIP95(III: 564-567).
IEEE DOI 9510
BibRef

Ashton, E.A., Berg, M.J., Parker, K.J., Weisberg, J., Chen, C.W.[Chang Wen], Ketonen, L.,
Segmentation and features extraction techniques, with applications to biomedical images,
ICIP94(III: 726-730).
IEEE DOI 9411
BibRef

Ashton, E.A., Molinelli, L., Totterman, S., Parker, K.J.,
Evaluation of reproducibility for manual and semi-automated feature extraction in CT and MR images,
ICIP02(III: 161-164).
IEEE DOI 0210
BibRef

Chang, J.[Jenghwa], Graber, H.L., Koo, P.C.[Ping Chen], Aronson, R., Barbour, S.L.S., Barbour, R.L.,
Optical imaging of anatomical maps derived from magnetic resonance images using time-independent optical sources,
MedImg(16), No. 1, February 1997, pp. 68-77.
IEEE Top Reference. 0205
BibRef

Pham, D.L.[Dzung L.], Prince, J.L.[Jerry L.],
An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities,
PRL(20), No. 1, January 1999, pp. 57-68. BibRef 9901

Pham, D.L., Prince, J.L.,
Adaptive fuzzy segmentation of magnetic resonance images,
MedImg(18), No. 9, September 1999, pp. 737-752.
IEEE Top Reference. 0110
BibRef

Pham, D.L.[Dzung L.],
Fuzzy Fractal Analysis of Molecular Imaging Data,
PIEEE(96), No. 8, August 2008, pp. 1332-1347.
IEEE DOI 0804
BibRef

Pham, D.L.[Dzung L.],
Spatial Models for Fuzzy Clustering,
CVIU(84), No. 2, November 2001, pp. 285-297.
DOI Link 0203
BibRef
Earlier:
Fuzzy clustering with spatial constraints,
ICIP02(II: 65-68).
IEEE DOI 0210
BibRef
Earlier:
Edge-adaptive Clustering for Unsupervised Image Segmentation,
ICIP00(Vol I: 816-819).
IEEE DOI 0008
BibRef

Hiltner, J., Fathi, M., Reusch, B.,
An approach to use linguistic and model-based fuzzy expert knowledge for the analysis of MRT images,
IVC(19), No. 4, March 2001, pp. 195-206.
Elsevier DOI 0102
BibRef
And: Erratum: IVC(19), No. 13, November 2001, pp. 1021.
Elsevier DOI 0111
BibRef

Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.,
A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data,
MedImg(21), No. 3, March 2002, pp. 193-199.
IEEE Top Reference. 0205
BibRef

Ahmed, M.N., Yamany, S.M., Farag, A.A., Moriarty, T.,
Bias Field Estimation and Adaptive Segmentation of MRI Data Using a Modified Fuzzy C-Means Algorithm,
CVPR99(I: 250-255).
IEEE DOI BibRef 9900

Tang, M.X.[Meng-Xing], Wang, W.[Wei], Wheeler, J., McCormick, M., Dong, X.Z.[Xiu-Zhen],
Effects of incompatible boundary information in EIT on the convergence behavior of an iterative algorithm,
MedImg(21), No. 6, June 2002, pp. 620-628.
IEEE Top Reference. 0208
BibRef

Menegaz, G., Thiran, J.P.,
Lossy to lossless object-based coding of 3-d MRI data,
IP(11), No. 9, September 2002, pp. 1053-1061.
IEEE DOI 0210
BibRef

Chen, Y.S.[Ya-Sheng], Amini, A.A.,
A MAP Framework for Tag Line Detection in SPAMM Data Using Markov Random Fields on the B-Spline Solid,
MedImg(21), No. 9, September 2002, pp. 1110-1122.
IEEE Top Reference. 0301
BibRef
Earlier: MMBIA01(xx-yy). 0110
BibRef

Chavez, S., Xiang, Q.S.[Qing-San], An, L.[Li],
Understanding phase maps in MRI: a new cutline phase unwrapping method,
MedImg(21), No. 8, August 2002, pp. 966-977.
IEEE Top Reference. 0301
BibRef

Rohr, K.[Karl],
Extraction of 3d anatomical point landmarks based on invariance principles,
PR(32), No. 1, January 1999, pp. 3-15.
Elsevier DOI
See also Evaluation of 3D Operators for the Detection of Anatomical Point Landmarks in MR and CT Images. BibRef 9901

Frantz, S.[Sönke], Rohr, K.[Karl], Stiehl, H.S.[H. Siegfried],
Development and validation of a multi-step approach to improved detection of 3D point landmarks in tomographic images,
IVC(23), No. 11, 1 October 2005, pp. 956-971.
Elsevier DOI 0510
BibRef
Earlier:
Multi-Step Procedures for the Localization of 2-D and 3-D Point Landmarks and Automatic ROI Size Selection,
ECCV98(I: 687).
Springer DOI BibRef

Wang, C.M.[Chuin-Mu], Chen, C.C.C.[Clayton Chi-Chang], Chung, Y.N.[Yi-Nung], Yang, S.C.[Sheng-Chih], Chung, P.C.[Pau-Choo], Yang, C.W.[Ching-Wen], Chang, C.I.[Chein-I.],
Detection of spectral signatures in multispectral MR images for classification,
MedImg(22), No. 1, January 2003, pp. 50-61.
IEEE Top Reference. 0304
BibRef

Basser, P.J., Pajevic, S.,
A Normal Distribution for Tensor-Valued Random Variables: Applications to Diffusion Tensor MRI,
MedImg(22), No. 7, July 2003, pp. 785-794.
IEEE Abstract. 0308
BibRef

Liew, A.W.C., Yan, H.[Hong],
An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation,
MedImg(22), No. 9, September 2003, pp. 1063-1075.
IEEE Abstract. 0309
BibRef

Sato, Y., Tanaka, H., Nishii, T., Nakanishi, K., Sugano, N., Kubota, T., Nakamura, H., Yoshikawa, H., Ochi, T., Tamura, S.,
Limits on the accuracy of 3-D thickness measurement in magnetic resonance images- Effects of voxel anisotropy,
MedImg(22), No. 9, September 2003, pp. 1076-1088.
IEEE Abstract. 0309
BibRef

Carballido-Gamio, J., Belongie, S.J., Majumdar, S.,
Normalized Cuts in 3-D for Spinal MRI Segmentation,
MedImg(23), No. 1, January 2004, pp. 36-44.
IEEE Abstract. 0403
BibRef

Fournial, R., Traore, A.S., Laurendeau, D., Moisan, C.,
An Analytic Method to Predict the Thermal Map of Cryosurgery Iceballs in MR Images,
MedImg(23), No. 1, January 2004, pp. 122-129.
IEEE Abstract. 0403
BibRef

Chan, D.Y.[Din-Yuen], Cheng, H.Y., Hsieh, H.L.[Hsin-Lung],
Tissue separation in MR images: From supervised to unsupervised classification,
JVCIR(15), No. 2, June 2004, pp. 185-202.
Elsevier DOI 0405
BibRef

Gilchrist, C.L., Xia, J.Q., Setton, L.A., Hsu, E.W.,
High-resolution determination of soft tissue deformations using MRI and first-order texture correlation,
MedImg(23), No. 5, May 2004, pp. 546-553.
IEEE Abstract. 0406
BibRef

Ng, S.K.[Shu-Kay], McLachlan, G.J.[Geoffrey J.],
Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images,
PR(37), No. 8, August 2004, pp. 1573-1589.
Elsevier DOI 0407
BibRef

Wang, Z.Z., Vemuri, B.C., Chen, Y., Mareci, T.H.,
A Constrained Variational Principle for Direct Estimation and Smoothing of the Diffusion Tensor Field From Complex DWI,
MedImg(23), No. 8, August 2004, pp. 930-939.
IEEE Abstract. 0409
BibRef
Earlier:
Simultaneous smoothing and estimation of the tensor field from diffusion tensor MRI,
CVPR03(I: 461-466).
IEEE DOI 0307
BibRef

Wang, Z.Z.[Zhi-Zhou], Vemuri, B.C.[Baba C.],
An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation,
CVPR04(I: 228-233).
IEEE DOI 0408
BibRef
And:
Tensor Field Segmentation Using Region Based Active Contour Model,
ECCV04(Vol IV: 304-315).
Springer DOI 0405
BibRef

Chen, Y., Guo, W., Zeng, Q., Yan, X., Huang, F., Zhang, H., He, G., Vemuri, B.C., Liu, Y.,
Estimation, smoothing, and characterization of apparent diffusion coefficient profiles from high angular resolution DWI,
CVPR04(I: 588-593).
IEEE DOI 0408
Diffusion Weighted MRI. BibRef

Kannan, S.R.,
A New Clustering Algorith for Segmentation of Magnetic Resonance Images Using Fuzzy C-Mean and Computer Programming,
GVIP(05), No. V2, January 2005, pp. 17-23
HTML Version. BibRef 0501

Buonocore, M.H., Katzberg, R.W.,
Estimation of Extraction Fraction (EF) and Glomerular Filtration Rate (GFR) Using MRI: Considerations Derived From a New Gd-Chelate Biodistribution Model Simulation,
MedImg(24), No. 5, May 2005, pp. 651-666.
IEEE Abstract. 0505
BibRef

Soleimani, M., Powell, C.E., Polydorides, N.,
Improving the Forward Solver for the Complete Electrode Model in EIT Using Algebraic Multigrid,
MedImg(24), No. 5, May 2005, pp. 577-583.
IEEE Abstract. 0505
electrical impedance tomography BibRef

Srikanth, R., Ramakrishnan, A.G.,
Contextual Encoding in Uniform and Adaptive Mesh-Based Lossless Compression of MR Images,
MedImg(24), No. 9, September 2005, pp. 1199-1206.
IEEE DOI 0509
BibRef

Wang, Z., Vemuri, B.C.,
DTI Segmentation Using an Information Theoretic Tensor Dissimilarity Measure,
MedImg(24), No. 10, October 2005, pp. 1267-1277.
IEEE DOI 0510
Diffusion Tensor Imaging. BibRef

Hung, W.L.[Wen-Liang], Yang, M.S.[Miin-Shen], Chen, D.H.[De-Hua],
Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation,
PRL(27), No. 5, 1 April 2006, pp. 424-438.
Elsevier DOI Fuzzy clustering; Fuzzy c-means; Suppressed fuzzy c-means; Parameter selection; Magnetic resonance image segmentation 0604
BibRef

Hung, W.L.[Wen-Liang], Yang, M.S.[Miin-Shen], Chen, D.H.[De-Hua],
Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation,
PRL(29), No. 9, 1 July 2008, pp. 1317-1325.
Elsevier DOI 0711
Fuzzy clustering; Fuzzy c-means; Weighted fuzzy c-means; Bootstrap; Variation; Color image segmentation BibRef

Raj, A.[Ashish], Wang, Y., Zabih, R.,
A Maximum Likelihood Approach to Parallel Imaging With Coil Sensitivity Noise,
MedImg(26), No. 8, August 2007, pp. 1046-1057.
IEEE DOI 0709
BibRef

Raj, A.[Ashish], Singh, G.[Gurmeet], Zabih, R.[Ramin],
MRFs for MRIs: Bayesian Reconstruction of MR Images via Graph Cuts,
CVPR06(I: 1061-1068).
IEEE DOI 0606
BibRef

Soleimani, M., Lionheart, W.R.B.,
Absolute Conductivity Reconstruction in Magnetic Induction Tomography Using a Nonlinear Method,
MedImg(25), No. 12, December 2006, pp. 1521-1530.
IEEE DOI 0701
BibRef

Vovk, U., Pernus, F.[Franjo], Likar, B.[Bostjan],
A Review of Methods for Correction of Intensity Inhomogeneity in MRI,
MedImg(26), No. 3, March 2007, pp. 405-421.
IEEE DOI 0703
BibRef

Kindlmann, G., Ennis, D.B., Whitaker, R.T., Westin, C.F.[Carl-Fredrik],
Diffusion Tensor Analysis With Invariant Gradients and Rotation Tangents,
MedImg(26), No. 11, November 2007, pp. 1483-1499.
IEEE DOI 0709
BibRef

Awate, S.P., Zhang, H., Gee, J.C.,
A Fuzzy, Nonparametric Segmentation Framework for DTI and MRI Analysis: With Applications to DTI-Tract Extraction,
MedImg(26), No. 11, November 2007, pp. 1525-1536.
IEEE DOI 0709
BibRef

Fonteijn, H.M.J., Verstraten, F.A.J., Norris, D.G.,
Probabilistic Inference on Q-ball Imaging Data,
MedImg(26), No. 11, November 2007, pp. 1515-1524.
IEEE DOI 0709
MRI issue. BibRef

Freidlin, R.Z., Ãzarslan, E., Komlosh, M.E., Chang, L.C., Koay, C.G., Jones, D.K., Basser, P.J.,
Parsimonious Model Selection for Tissue Segmentation and Classification Applications: A Study Using Simulated and Experimental DTI Data,
MedImg(26), No. 11, November 2007, pp. 1576-1584.
IEEE DOI 0709
BibRef

Dharmaraj, C.D.[Christopher D.], Krishna, M.C.[Murali C.], Murugesan, R.,
A Feature Identification System for Electron Magnetic Resonance Tomography: Fusion of Principal Components Transform, Color Quantization and Boundary Information,
JMIV(30), No. 3, March 2008, pp. 284-297.
Springer DOI 0802
BibRef

Duchesne, S., Caroli, A., Geroldi, C., Barillot, C., Frisoni, G.B., Collins, D.L.,
MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls,
MedImg(27), No. 4, April 2008, pp. 509-520.
IEEE DOI 0804
BibRef

Niedre, M., Ntziachristos, V.,
Elucidating Structure and Function In Vivo With Hybrid Fluorescence and Magnetic Resonance Imaging,
PIEEE(96), No. 3, March 2008, pp. 382-396.
IEEE DOI 0804
BibRef

Zalesky, A.,
DT-MRI Fiber Tracking: A Shortest Paths Approach,
MedImg(27), No. 10, October 2008, pp. 1458-1471.
IEEE DOI 0810
BibRef

Jacob, M., Sutton, B.P.,
Algebraic Decomposition of Fat and Water in MRI,
MedImg(28), No. 2, February 2009, pp. 173-184.
IEEE DOI 0902
BibRef

Bresch, E., Narayanan, S.,
Region Segmentation in the Frequency Domain Applied to Upper Airway Real-Time Magnetic Resonance Images,
MedImg(28), No. 3, March 2009, pp. 323-338.
IEEE DOI 0903
BibRef

Shilling, R.Z., Robbie, T.Q., Bailloeul, T., Mewes, K., Mersereau, R.M., Brummer, M.E.[Marijn E.],
A Super-Resolution Framework for 3-D High-Resolution and High-Contrast Imaging Using 2-D Multislice MRI,
MedImg(28), No. 5, May 2009, pp. 633-644.
IEEE DOI 0905
BibRef

Shilling, R.Z.[Richard Z.], Ramamurthy, S.[Senthil], Brummer, M.E.[Marijn E.],
Sampling strategies for super-resolution in multi-slice MRI,
ICIP08(2240-2243).
IEEE DOI 0810
BibRef

Lee, J.D., Su, H.R., Cheng, P.E., Liou, M., Aston, J.A.D., Tsai, A.C., Chen, C.Y.,
MR Image Segmentation Using a Power Transformation Approach,
MedImg(28), No. 6, June 2009, pp. 894-905.
IEEE DOI 0906
BibRef

Withey, D.J., Pedrycz, W., Koles, Z.J.,
Dynamic edge tracing: Boundary identification in medical images,
CVIU(113), No. 10, October 2009, pp. 1039-1052.
Elsevier DOI 0910
Image segmentation; Medical image analysis; Edge tracing; Kalman filter; Target tracking; Magnetic resonance imaging BibRef

Kressler, B., de Rochefort, L., Liu, T., Spincemaille, P., Jiang, Q., Wang, Y.,
Nonlinear Regularization for Per Voxel Estimation of Magnetic Susceptibility Distributions From MRI Field Maps,
MedImg(29), No. 2, February 2010, pp. 273-281.
IEEE DOI 1002
BibRef

Liu, T., Xu, W., Spincemaille, P., Avestimehr, A.S., Wang, Y.,
Accuracy of the Morphology Enabled Dipole Inversion (MEDI) Algorithm for Quantitative Susceptibility Mapping in MRI,
MedImg(31), No. 3, March 2012, pp. 816-824.
IEEE DOI 1203
BibRef

Zhang, X., Zhu, S., He, B.,
Imaging Electric Properties of Biological Tissues by RF Field Mapping in MRI,
MedImg(29), No. 2, February 2010, pp. 474-481.
IEEE DOI 1002
BibRef

Ng, H.P., Ong, S.H., Huang, S., Liu, J., Foong, K.W.C., Goh, P.S., Nowinski, W.L.,
Salient features useful for the accurate segmentation of masticatory muscles from minimum slices subsets of magnetic resonance images,
MVA(21), No. 4, June 2010, pp. xx-yy.
Springer DOI 1006
BibRef

Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S., Nowinski, W.L.,
Automatic Segmentation of Muscles of Mastication from Magnetic Resonance Images Using Prior Knowledge,
ICPR06(III: 968-971).
IEEE DOI 0609
BibRef
Earlier: A1, A2, A4, A3, A5:
Template-based Automatic Segmentation of Masseter Using Prior Knowledge,
Southwest06(208-212).
IEEE DOI 0603
BibRef

Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S., Nowinski, W.L.,
Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm,
Southwest06(61-65).
IEEE DOI 0603
BibRef

Duits, R.[Remco], Franken, E.[Erik],
Left-Invariant Diffusions on the Space of Positions and Orientations and their Application to Crossing-Preserving Smoothing of HARDI images,
IJCV(92), No. 3, May 2011, pp. 231-264.
WWW Link. 1103
High Angular Resolution Diffusion Images BibRef

Reisert, M., Kiselev, V.G.,
Fiber Continuity: An Anisotropic Prior for ODF Estimation,
MedImg(30), No. 6, June 2011, pp. 1274-1283.
IEEE DOI 1101
HARDI data. BibRef

Reisert, M., Kellner, E., Kiselev, V.G.,
About the Geometry of Asymmetric Fiber Orientation Distributions,
MedImg(31), No. 6, June 2012, pp. 1240-1249.
IEEE DOI 1206
BibRef

Huang, F., Narayan, S., Wilson, D., Johnson, D., Zhang, G.Q.,
A Fast Iterated Conditional Modes Algorithm for Water-Fat Decomposition in MRI,
MedImg(30), No. 8, August 2011, pp. 1480-1492.
IEEE DOI 1108
BibRef

Kranjc, M., Bajd, F., Sersa, I., Miklavcic, D.,
Magnetic Resonance Electrical Impedance Tomography for Monitoring Electric Field Distribution During Tissue Electroporation,
MedImg(30), No. 10, October 2011, pp. 1771-1778.
IEEE DOI 1110
BibRef

Fouquier, G.[Geoffroy], Atif, J.[Jamal], Bloch, I.[Isabelle],
Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations,
CVIU(116), No. 1, January 2012, pp. 146-165.
Elsevier DOI 1112
Segmentation; Knowledge-based system; Spatial relations; Graph representations; Fuzzy sets; Medical images; MRI BibRef

Fouquier, G.[Geoffroy], Anquez, J.[Jérémie], Bloch, I.[Isabelle], Falip, C.[Céline], Adamsbaum, C.[Catherine],
Subcutaneous Adipose Tissue Segmentation in Whole-Body MRI of Children,
CIARP11(97-104).
Springer DOI 1111
BibRef

Chinnadurai, V.[Vijayakumar], Chandrashekhar, G.D.[Gharpure Damayanti],
Neuro-levelset system based segmentation in dynamic susceptibility contrast enhanced and diffusion weighted magnetic resonance images,
PR(45), No. 9, September 2012, pp. 3501-3511.
Elsevier DOI 1206
Neuro-levelset method; Artificial neural networks; Radial basis function; Self-organizing map; Dynamic contrast susceptibility magnetic resonance images; Diffusion weighted images BibRef

Rivest-Henault, D., Cheriet, M.[Mohamed],
3-D Curvilinear Structure Detection Filter Via Structure-Ball Analysis,
IP(22), No. 7, 2013, pp. 2849-2863.
IEEE DOI 1307
biological tissues; biomedical MRI BibRef

Tran, L.[Loc], Banerjee, D.[Debrup], Wang, J.H.[Ji-Hong], Kumar, A.J.[Ashok J.], McKenzie, F.[Frederic], Li, Y.H.[Yao-Hang], Li, J.[Jiang],
High-dimensional MRI data analysis using a large-scale manifold learning approach,
MVA(24), No. 5, July 2013, pp. 995-1014.
Springer DOI 1306
BibRef

Qiu, C.Y.[Cun-Yong], Xiao, J.[Jian], Yu, L.[Long], Han, L.[Lu], Iqbal, M.N.[Muhammad Naveed],
A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation,
PRL(34), No. 12, 1 September 2013, pp. 1329-1338.
Elsevier DOI 1306
Image segmentation; Magnetic resonance imaging; Fuzzy C-means; Interval type-2 fuzzy sets BibRef

Qiu, C.Y.[Cun-Yong], Xiao, J.[Jian], Han, L.[Lu], Iqbal, M.N.[Muhammad Naveed],
Enhanced interval type-2 fuzzy c-means algorithm with improved initial center,
PRL(38), No. 1, 2014, pp. 86-92.
Elsevier DOI 1402
Fuzzy clustering BibRef

Malgina, O., Praznikar, A., Tasic, J.F.,
Inhomogeneity correction and fat-tissue extraction in MR images of FacioScapuloHumeral muscular Dystrophy,
PRL(34), No. 12, 1 September 2013, pp. 1364-1371.
Elsevier DOI 1306
Magnetic resonance imaging; Fat tissue; Muscle tissue; Bias field; Inhomogeneity correction BibRef

Rondina, J.M., Hahn, T., de Oliveira, L., Marquand, A.F., Dresler, T., Leitner, T., Fallgatter, A.J., Shawe-Taylor, J., Mourao-Miranda, J.,
SCoRS: A Method Based on Stability for Feature Selection and Apping in Neuroimaging,
MedImg(33), No. 1, January 2014, pp. 85-98.
IEEE DOI 1402
BibRef
And: Correction: MedImg(33), No. 3, March 2014, pp. 794-794.
IEEE DOI 1404
biomedical MRI BibRef

Cetingul, H.E., Wright, M.J., Thompson, P.M., Vidal, R.,
Segmentation of High Angular Resolution Diffusion MRI Using Sparse Riemannian Manifold Clustering,
MedImg(33), No. 2, February 2014, pp. 301-317.
IEEE DOI 1403
biodiffusion BibRef

Yaqub, M., Javaid, M.K., Cooper, C., Noble, J.A.,
Investigation of the Role of Feature Selection and Weighted Voting in Random Forests for 3-D Volumetric Segmentation,
MedImg(33), No. 2, February 2014, pp. 258-271.
IEEE DOI 1403
biomedical MRI BibRef

Gao, J.J.[Jing-Jing], Xie, M.[Mei], Mao, L.[Ling],
Interleaved k-NN Classification and Bias Field Estimation for MR Image with Intensity Inhomogeneity,
IEICE(E97-D), No. 4, April 2014, pp. 1011-1015.
WWW Link. 1404
BibRef

Zhao, B.[Bo], Lam, F.[Fan], Liang, Z.P.[Zhi-Pei],
Model-Based MR Parameter Mapping With Sparsity Constraints: Parameter Estimation and Performance Bounds,
MedImg(33), No. 9, September 2014, pp. 1832-1844.
IEEE DOI 1410
biological tissues BibRef

Akhondi-Asl, A., Hoyte, L., Lockhart, M.E., Warfield, S.K.,
A Logarithmic Opinion Pool Based STAPLE Algorithm for the Fusion of Segmentations With Associated Reliability Weights,
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IEEE DOI 1411
biomedical MRI BibRef

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multimodal MRI BibRef

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IJIST(25), No. 1, 2015, pp. 102-113.
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DTI BibRef

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A Statistical Method for Improved 3D Surface Detection,
SPLetters(22), No. 8, August 2015, pp. 1045-1049.
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edge detection BibRef

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Frequency-Dependent Conductivity Contrast for Tissue Characterization Using a Dual-Frequency Range Conductivity Mapping Magnetic Resonance Method,
MedImg(34), No. 2, February 2015, pp. 507-513.
IEEE DOI 1502
Animals BibRef

Lee, S.K.[Seung-Kyun], Bulumulla, S., Wiesinger, F., Sacolick, L., Sun, W., Hancu, I.,
Tissue Electrical Property Mapping From Zero Echo-Time Magnetic Resonance Imaging,
MedImg(34), No. 2, February 2015, pp. 541-550.
IEEE DOI 1502
Coils BibRef

Lee, S.K.[Seung-Kyun], Bulumulla, S., Hancu, I.,
Theoretical Investigation of Random Noise-Limited Signal-to-Noise Ratio in MR-Based Electrical Properties Tomography,
MedImg(34), No. 11, November 2015, pp. 2220-2232.
IEEE DOI 1512
bioelectric phenomena BibRef

Al-Hinnawi, A.R.[Abdel Razzak], Daear, M.[Mohammed],
Assessment of bilateral filter on low NEX open MRI views,
SIViP(9), No. 1, January 2015, pp. 9-17.
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Xu, X., Lee, K., Zhang, L., Sonka, M., Abramoff, M.D.,
Stratified Sampling Voxel Classification for Segmentation of Intraretinal and Subretinal Fluid in Longitudinal Clinical OCT Data,
MedImg(34), No. 7, July 2015, pp. 1616-1623.
IEEE DOI 1507
Anisotropic magnetoresistance BibRef

Ahmadvand, A.[Ali], Kabiri, P.[Peyman],
Multispectral MRI image segmentation using Markov random field model,
SIViP(10), No. 1, February 2016, pp. 251-258.
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Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., Koethe, U., Kleesiek, J., Stieltjes, B., Maier-Hein, K.H.,
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images,
MedImg(35), No. 1, January 2016, pp. 184-196.
IEEE DOI 1601
Image segmentation BibRef

Pratondo, A., Chui, C.K.[Chee-Kong], Ong, S.H.[Sim-Heng],
Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation,
SPLetters(23), No. 2, February 2016, pp. 222-226.
IEEE DOI 1602
biomedical MRI BibRef

Lê, M., Delingette, H., Kalpathy-Cramer, J., Gerstner, E.R., Batchelor, T., Unkelbach, J., Ayache, N.,
MRI Based Bayesian Personalization of a Tumor Growth Model,
MedImg(35), No. 10, October 2016, pp. 2329-2339.
IEEE DOI 1610
Bayes methods BibRef

Blaiotta, C.[Claudia], Cardoso, M.J.[M. Jorge], Ashburner, J.[John],
Variational inference for medical image segmentation,
CVIU(151), No. 1, 2016, pp. 14-28.
Elsevier DOI 1610
Image segmentation BibRef

Tan, C.W.[Chao-Wei], Li, K.[Kang], Yan, Z.N.[Zhen-Nan], Yang, D.[Dong], Zhang, S.T.[Shao-Ting], Yu, H.J.[Hui Jing], Engelke, K.[Klaus], Miller, C.[Colin], Metaxas, D.N.[Dimitris N.],
A detection-driven and sparsity-constrained deformable model for fascia lata labeling and thigh inter-muscular adipose quantification,
CVIU(151), No. 1, 2016, pp. 80-89.
Elsevier DOI 1610
Thigh inter-muscular adipose tissue quantification BibRef

van Niekerk, A., van der Kouwe, A., Meintjes, E.,
A Method for Measuring Orientation Within a Magnetic Resonance Imaging Scanner Using Gravity and the Static Magnetic Field (VectOrient),
MedImg(36), No. 5, May 2017, pp. 1129-1139.
IEEE DOI 1705
Gravity, Magnetic domains, Magnetic resonance imaging, Magnetometers, Position measurement, Tracking, Accelerometer, MRI, angular rate, magnetometer, orientation, prospective, motion, correction BibRef

Bao, S., Chung, A.C.S.,
Feature Sensitive Label Fusion With Random Walker for Atlas-Based Image Segmentation,
IP(26), No. 6, June 2017, pp. 2797-2810.
IEEE DOI 1705
Biomedical imaging, Feature extraction, Image analysis, Image registration, Image segmentation, Labeling, Sensitivity, Segmentation, brain, magnetic resonance imaging BibRef

Zhang, L., Cobzas, D., Wilman, A.H., Kong, L.,
Significant Anatomy Detection Through Sparse Classification: A Comparative Study,
MedImg(37), No. 1, January 2018, pp. 128-137.
IEEE DOI 1801
biomedical MRI, graph theory, image classification, medical image processing, neurophysiology, voxel based analysis BibRef

Nongmeikapam, K.[Kishorjit], Kumar, W.K.[Wahengbam Kanan], Singh, A.D.[Aheibam Dinamani],
Fast and Automatically Adjustable GRBF Kernel Based Fuzzy C-Means for Cluster-wise Coloured Feature Extraction and Segmentation of MR Images,
IET-IPR(12), No. 4, April 2018, pp. 513-524.
DOI Link 1804
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Daniels, C.J., Gallagher, F.A.,
Unsupervised Segmentation of 5D Hyperpolarized Carbon-13 MRI Data Using a Fuzzy Markov Random Field Model,
MedImg(37), No. 4, April 2018, pp. 840-850.
IEEE DOI 1804
Biochemistry, Image segmentation, In vivo, Magnetic resonance imaging, Markov processes, Tumors, markov random field BibRef

Wang, G.T.[Guo-Tai], Li, W.Q.[Wen-Qi], Zuluaga, M.A.[Maria A.], Pratt, R.[Rosalind], Patel, P.A.[Premal A.], Aertsen, M.[Michael], Doel, T.[Tom], David, A.L.[Anna L.], Deprest, J.[Jan], Ourselin, S.[Sébastien], Vercauteren, T.[Tom],
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning,
MedImg(37), No. 7, July 2018, pp. 1562-1573.
IEEE DOI 1808
biomedical MRI, convolution, feedforward neural nets, human computer interaction, image segmentation, brain tumor BibRef

Wang, G.T.[Guo-Tai], Zuluaga, M.A.[Maria A.], Li, W.Q.[Wen-Qi], Pratt, R.[Rosalind], Patel, P.A.[Premal A.], Aertsen, M.[Michael], Doel, T.[Tom], David, A.L.[Anna L.], Deprest, J.[Jan], Ourselin, S.[Sébastien], Vercauteren, T.[Tom],
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation,
PAMI(41), No. 7, July 2019, pp. 1559-1572.
IEEE DOI 1906
Image segmentation, Biomedical imaging, Image resolution, conditional random fields BibRef

Ramos-Llordén, G., Vegas-Sánchez-Ferrero, G., Björk, M., Vanhevel, F., Parizel, P.M., San José Estépar, R., den Dekker, A.J., Sijbers, J.,
NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRI T_1 Mapping,
MedImg(37), No. 11, November 2018, pp. 2414-2427.
IEEE DOI 1811
Optimization, Estimation, Approximation algorithms, Magnetic resonance imaging, Steady-state, Computational modeling, relaxometry BibRef

Cai, J.Z.[Jin-Zheng], Xing, F.Y.[Fu-Yong], Batra, A.[Abhinandan], Liu, F.J.[Fu-Jun], Walter, G.A.[Glenn A.], Vandenborne, K.[Krista], Yang, L.[Lin],
Texture analysis for muscular dystrophy classification in MRI with improved class activation mapping,
PR(86), 2019, pp. 368-375.
Elsevier DOI 1811
Muscular dystrophy, Convolutional neural network, MRI analysis, Texture classification, Abnormality detection BibRef

Maidens, J., Gordon, J.W., Chen, H., Park, I., van Criekinge, M., Milshteyn, E., Bok, R., Aggarwal, R., Ferrone, M., Slater, J.B., Kurhanewicz, J., Vigneron, D.B., Arcak, M., Larson, P.E.Z.,
Spatio-Temporally Constrained Reconstruction for Hyperpolarized Carbon-13 MRI Using Kinetic Models,
MedImg(37), No. 12, December 2018, pp. 2603-2612.
IEEE DOI 1812
Magnetic resonance imaging, Optimization, Data models, Substrates, Convex functions, Image reconstruction, Correlation, molecular imaging BibRef

Tashan, T.[Tariq], Al-Azawi, M.[Maher],
Multilevel magnetic resonance imaging compression using compressive sensing,
IET-IPR(12), No. 12, December 2018, pp. 2186-2191.
DOI Link 1812
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Christiaens, D., Cordero-Grande, L., Hutter, J., Price, A.N., Deprez, M., Hajnal, J.V., Tournier, J.,
Learning Compact q-Space Representations for Multi-Shell Diffusion-Weighted MRI,
MedImg(38), No. 3, March 2019, pp. 834-843.
IEEE DOI 1903
Harmonic analysis, Covariance matrices, Signal representation, Magnetic resonance imaging, Microstructure, Image resolution, dimensionality reduction BibRef

Grigorescu, I.[Irina], Uus, A.[Alena], Christiaens, D.[Daan], Cordero-Grande, L.[Lucilio], Hutter, J.[Jana], Edwards, A.D.[A. David], Hajnal, J.V.[Joseph V.], Modat, M.[Marc], Deprez, M.[Maria],
Diffusion Tensor Driven Image Registration: A Deep Learning Approach,
WBIR20(131-140).
Springer DOI 2006
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Deprez, M., Price, A., Christiaens, D., Lockwood Estrin, G., Cordero-Grande, L., Hutter, J., Daducci, A., Tournier, J., Rutherford, M., Counsell, S.J., Cuadra, M.B., Hajnal, J.V.,
Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction,
MedImg(39), No. 4, April 2020, pp. 1104-1113.
IEEE DOI 2004
Image reconstruction, Magnetic resonance imaging, Distortion, Harmonic analysis, Nonhomogeneous media, Mathematical model, tractography BibRef

Mehrtash, A., Ghafoorian, M., Pernelle, G., Ziaei, A., Heslinga, F.G., Tuncali, K., Fedorov, A., Kikinis, R., Tempany, C.M., Wells, W.M., Abolmaesumi, P., Kapur, T.,
Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy,
MedImg(38), No. 4, April 2019, pp. 1026-1036.
IEEE DOI 1904
Needles, Magnetic resonance imaging, Biopsy, Image segmentation, Trajectory, Cancer, Observers, Convolutional neural networks, biopsy BibRef

Huo, Y., Xu, Z., Moon, H., Bao, S., Assad, A., Moyo, T.K., Savona, M.R., Abramson, R.G., Landman, B.A.,
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth,
MedImg(38), No. 4, April 2019, pp. 1016-1025.
IEEE DOI 1904
Image segmentation, Magnetic resonance imaging, Computed tomography, Image generation, Training, Manuals, Synthesis, convolutional BibRef

Huo, Y., Xu, Z., Bao, S., Bermudez, C., Moon, H., Parvathaneni, P., Moyo, T.K., Savona, M.R., Assad, A., Abramson, R.G., Landman, B.A.,
Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks,
MedImg(38), No. 5, May 2019, pp. 1185-1196.
IEEE DOI 1905
Image segmentation, Magnetic resonance imaging, Kernel, splenomegaly BibRef

Kaushik, S.[Sumit], Slovák, J.[Jan],
HARDI Segmentation via Fourth-Order Tensors and Anisotropy Preserving Similarity Measures,
JMIV(61), No. 8, October 2019, pp. 1221-1234.
WWW Link. 1909
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Yan, W., Wang, Y., Xia, M., Tao, Q.,
Edge-Guided Output Adaptor: Highly Efficient Adaptation Module for Cross-Vendor Medical Image Segmentation,
SPLetters(26), No. 11, November 2019, pp. 1593-1597.
IEEE DOI 1911
biomedical MRI, convolutional neural nets, edge detection, image segmentation, learning (artificial intelligence), edge detection BibRef

Jatla, V., Pattichis, M.S., Arge, C.N.,
Image Processing Methods for Coronal Hole Segmentation, Matching, and Map Classification,
IP(29), No. , 2020, pp. 1641-1653.
IEEE DOI 1911
Image segmentation, Magnetic resonance imaging, Integrated circuit modeling, Atmospheric modeling, Manuals, random forests BibRef

Moutal, N., Maximov, I.I., Grebenkov, D.S.,
Probing Surface-to-Volume Ratio of an Anisotropic Medium by Diffusion NMR with General Gradient Encoding,
MedImg(38), No. 11, November 2019, pp. 2507-2522.
IEEE DOI 1911
Anisotropic magnetoresistance, Encoding, Shape, Surface waves, Nuclear magnetic resonance, Microscopy, Radio frequency, anisotropy BibRef

Vigneshwaran, S., Govindaraj, V.[Vishnuvarthanan], Murugan, P.R.[Pallikonda R.], Zhang, Y.D.[Yu-Dong], Prasath, T.A.[Thiyagarajan Arun],
Unsupervised learning-based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging,
IJIST(29), No. 4, 2019, pp. 439-456.
DOI Link 1911
medical image analysis, modified fuzzy K-means, self-organizing map, tissue segmentation, tumors and lesion identification BibRef

Abdullah Al, W.[Walid], Yun, I.D.[Il Dong],
Partial Policy-Based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images,
MedImg(39), No. 4, April 2020, pp. 1245-1255.
IEEE DOI 2004
Reinforcement learning, Biomedical imaging, Training, Search problems, reinforcement learning BibRef

Biffi, C., Cerrolaza, J.J., Tarroni, G., Bai, W., de Marvao, A., Oktay, O., Ledig, C., Le Folgoc, L., Kamnitsas, K., Doumou, G., Duan, J., Prasad, S.K., Cook, S.A., O'Regan, D.P., Rueckert, D.,
Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models,
MedImg(39), No. 6, June 2020, pp. 2088-2099.
IEEE DOI 2006
Shape analysis, explainable deep learning, generative modeling, MRI BibRef

Dou, Q., Liu, Q., Heng, P.A., Glocker, B.,
Unpaired Multi-Modal Segmentation via Knowledge Distillation,
MedImg(39), No. 7, July 2020, pp. 2415-2425.
IEEE DOI 2007
Magnetic resonance imaging, Computed tomography, Image segmentation, Semantics, Task analysis, Feature extraction, image segmentation BibRef

Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Wood, B.J., Roth, H., Myronenko, A., Xu, D., Xu, Z.,
Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation,
MedImg(39), No. 7, July 2020, pp. 2531-2540.
IEEE DOI 2007
Biomedical imaging, Training, Magnetic resonance imaging, Data models, Adaptation models, Image segmentation, Deep learning, medical image segmentation BibRef

Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.,
Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation,
MedImg(39), No. 7, July 2020, pp. 2494-2505.
IEEE DOI 2007
Image segmentation, Magnetic resonance imaging, Feature extraction, Biomedical imaging, Computed tomography, adversarial learning BibRef

Jiang, J.[Jue], Hu, Y.C., Tyagi, N., Rimner, A.[Andreas], Lee, N., Deasy, J.O.[Joseph O.], Berry, S., Veeraraghavan, H.[Harini],
PSIGAN: Joint Probabilistic Segmentation and Image Distribution Matching for Unpaired Cross-Modality Adaptation-Based MRI Segmentation,
MedImg(39), No. 12, December 2020, pp. 4071-4084.
IEEE DOI 2012
Image segmentation, Magnetic resonance imaging, Computed tomography, Training, Generators, Geometry, abdominal organs BibRef

Jiang, J.[Jue], Rimner, A.[Andreas], Deasy, J.O.[Joseph O.], Veeraraghavan, H.[Harini],
Unpaired Cross-Modality Educed Distillation (CMEDL) for Medical Image Segmentation,
MedImg(41), No. 5, May 2022, pp. 1057-1068.
IEEE DOI 2205
Magnetic resonance imaging, Computed tomography, Image segmentation, Feature extraction, Tumors, Training, lung tumor segmentation BibRef

Zhu, J.[Jiening], Veeraraghavan, H.[Harini], Jiang, J.[Jue], Oh, J.H.[Jung Hun], Norton, L.[Larry], Deasy, J.O.[Joseph O.], Tannenbaum, A.[Allen],
Wasserstein HOG: Local Directionality Extraction via Optimal Transport,
MedImg(43), No. 3, March 2024, pp. 916-927.
IEEE DOI 2403
Feature extraction, Computed tomography, Tumors, Entropy, Cancer, Radiomics, Magnetic resonance imaging, Optimal transport, MRI, CT, imaging processing BibRef

Govindaraj, V.[Vishnuvarthanan], Thiyagarajan, A.[Arunprasath], Rajasekaran, P.[Pallikonda], Zhang, Y.D.[Yu-Dong], Krishnasamy, R.[Rajesh],
Automated unsupervised learning-based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI),
IET-IPR(14), No. 14, December 2020, pp. 3516-3526.
DOI Link 2012
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Chartsias, A., Papanastasiou, G., Wang, C., Semple, S., Newby, D.E., Dharmakumar, R., Tsaftaris, S.A.,
Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation,
MedImg(40), No. 3, March 2021, pp. 781-792.
IEEE DOI 2103
Image segmentation, Biomedical imaging, Annotations, Training, Semantics, Decoding, Multimodal segmentation, disentanglement, magnetic resonance imaging BibRef

Nasor, M.[Mohamed], Obaid, W.[Walid],
Segmentation of osteosarcoma in MRI images by K-means clustering, Chan-Vese segmentation, and iterative Gaussian filtering,
IET-IPR(15), No. 6, 2021, pp. 1310-1318.
DOI Link 2106
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Molaie, M.[Malihe], Zoroofi, R.A.[Reza Aghaeizadeh],
Thigh muscle segmentation using a hybrid FRFCM-based multi-atlas method and morphology-based interpolation algorithm,
IET-IPR(15), No. 11, 2021, pp. 2572-2579.
DOI Link 2108
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Ji, S.[Sooyeon], Jeong, J.[Jinhee], Oh, S.H.[Se-Hong], Nam, Y.[Yoonho], Choi, S.H.[Seung Hong], Shin, H.G.[Hyeong-Geol], Shin, D.[Dongmyung], Jung, W.[Woojin], Lee, J.[Jongho],
Quad-Contrast Imaging: Simultaneous Acquisition of Four Contrast-Weighted Images (PD-Weighted, T2-Weighted, PD-FLAIR and T2-FLAIR Images) With Synthetic T1-Weighted Image, T1- and T2-Maps,
MedImg(40), No. 12, December 2021, pp. 3617-3626.
IEEE DOI 2112
Imaging, Image reconstruction, Magnetic resonance imaging, Radio frequency, Timing, Specific absorption rate, Encoding, multi-contrast imaging BibRef

Zhou, K.[Kang], Li, J.[Jing], Luo, W.X.[Wei-Xin], Li, Z.X.[Zheng-Xin], Yang, J.L.[Jian-Long], Fu, H.Z.[Hua-Zhu], Cheng, J.[Jun], Liu, J.[Jiang], Gao, S.H.[Sheng-Hua],
Proxy-Bridged Image Reconstruction Network for Anomaly Detection in Medical Images,
MedImg(41), No. 3, March 2022, pp. 582-594.
IEEE DOI 2203
Image reconstruction, Anomaly detection, Biomedical imaging, Retina, Feature extraction, Magnetic resonance imaging, Training, pseudo anomalies BibRef

Chen, C.[Cheng], Dou, Q.[Qi], Jin, Y.M.[Yue-Ming], Liu, Q.[Quande], Heng, P.A.[Pheng Ann],
Learning With Privileged Multimodal Knowledge for Unimodal Segmentation,
MedImg(41), No. 3, March 2022, pp. 621-632.
IEEE DOI 2203
Training, Magnetic resonance imaging, Image segmentation, Task analysis, Data models, Training data, Image synthesis, contrastive learning BibRef

Abulnaga, S.M.[S. Mazdak], Turk, E.A.[Esra Abaci], Bessmeltsev, M.[Mikhail], Grant, P.E.[P. Ellen], Solomon, J.[Justin], Golland, P.[Polina],
Volumetric Parameterization of the Placenta to a Flattened Template,
MedImg(41), No. 4, April 2022, pp. 925-936.
IEEE DOI 2204
Shape, Distortion, Magnetic resonance imaging, Visualization, Mesh generation, Strain, Anatomy visualization, injective map, volumetric mesh parameterization BibRef

Mo, S.C.[Shao-Cong], Cai, M.[Ming], Lin, L.F.[Lan-Fen], Tong, R.F.[Ruo-Feng], Chen, Q.Q.[Qing-Qing], Wang, F.[Fang], Hu, H.J.[Hong-Jie], Iwamoto, Y.[Yutaro], Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
Mutual Information-Based Graph Co-Attention Networks for Multimodal Prior-Guided Magnetic Resonance Imaging Segmentation,
CirSysVideo(32), No. 5, May 2022, pp. 2512-2526.
IEEE DOI 2205
Magnetic resonance imaging, Feature extraction, Lesions, Image segmentation, Liver, Fuses, Mutual information, MRI BibRef

Zhang, B.[Bo], Tan, Y.P.[Yun-Peng], Wang, H.[Hui], Zhang, Z.[Zheng], Zhou, X.Z.[Xiu-Zhuang], Wu, J.Y.[Jing-Yun], Mi, Y.[Yue], Huang, H.[Haiwen], Wang, W.D.[Wen-Dong],
LSRML: A latent space regularization based meta-learning framework for MR image segmentation,
PR(130), 2022, pp. 108821.
Elsevier DOI 2206
Latent space regularization, Meta learning, Domain generalization, Domain discriminator, Multi-source domain adaptation BibRef

Trombini, M.[Marco], Solarna, D.[David], Moser, G.[Gabriele], Dellepiane, S.[Silvana],
A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields,
PR(134), 2023, pp. 109082.
Elsevier DOI 2212
Graph signal processing, Segmentation, Markovian modeling, Parametric model estimation, Pattern recognition, Magnetic resonance imagery BibRef

Decaux, N.[Nathan], Conze, P.H.[Pierre-Henri], Ropars, J.[Juliette], He, X.[Xinyan], Sheehan, F.T.[Frances T.], Pons, C.[Christelle], Ben Salem, D.[Douraied], Brochard, S.[Sylvain], Rousseau, F.[François],
Semi-automatic muscle segmentation in MR images using deep registration-based label propagation,
PR(140), 2023, pp. 109529.
Elsevier DOI 2305
Semi-automatic segmentation, Musculoskeletal system, Label propagation, Deep registration BibRef

Yang, H.[Heran], Sun, J.[Jian], Xu, Z.B.[Zong-Ben],
Learning Unified Hyper-Network for Multi-Modal MR Image Synthesis and Tumor Segmentation With Missing Modalities,
MedImg(42), No. 12, December 2023, pp. 3678-3689.
IEEE DOI 2312
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Lobos, R.A.[Rodrigo A.], Chan, C.C.[Chin-Cheng], Haldar, J.P.[Justin P.],
New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI,
MedImg(43), No. 1, January 2024, pp. 286-296.
IEEE DOI 2401
BibRef

Autorino, M.M.[Maria Maddalena], Franceschini, S.[Stefano], Ambrosanio, M.[Michele], Pascazio, V.[Vito], Baselice, F.[Fabio],
Intra voxel analysis in magnetic resonance imaging via deep learning,
IJIST(34), No. 1, 2024, pp. e22977.
DOI Link 2401
deep learning, intra voxel analysis, magnetic resonance imaging, neural network, tissues discrimination BibRef

Javanbakhat, M.[Masoumeh], Starke, L.[Ludger], Waiczies, S.[Sonia], Lippert, C.[Christoph],
Quantifying model uncertainty for semantic segmentation of Fluorine-19 MRI using stochastic gradient MCMC,
CVIU(241), 2024, pp. 103967.
Elsevier DOI 2403
Fluorine-19 MRI, Segmentation, Deep learning, Uncertainty, MCMC BibRef

Fidon, L.[Lucas], Aertsen, M.[Michael], Kofler, F.[Florian], Bink, A.[Andrea], David, A.L.[Anna L.], Deprest, T.[Thomas], Emam, D.[Doaa], Guffens, F.[Fr©d©ric], Jakab, A.[Andr¡s], Kasprian, G.[Gregor], Kienast, P.[Patric], Melbourne, A.[Andrew], Menze, B.[Bjoern], Mufti, N.[Nada], Pogledic, I.[Ivana], Prayer, D.[Daniela], Stuempflen, M.[Marlene], van Elslander, E.[Esther], Ourselin, S.[S©bastien], Deprest, J.[Jan], Vercauteren, T.[Tom],
A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation,
PAMI(46), No. 5, May 2024, pp. 3784-3795.
IEEE DOI 2404
Artificial intelligence, Image segmentation, Contracts, Biomedical imaging, Magnetic resonance imaging, Training, out-of-domain generalization BibRef

Kim, J.[Jonghun], Park, H.[Hyunjin],
Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study,
WACV24(7589-7598)
IEEE DOI Code:
WWW Link. 2404
Adaptation models, Solid modeling, Image synthesis, Computational modeling, Magnetic resonance imaging, Switches BibRef

Antonelli, L.[Laura], de Simone, V.[Valentina], Viola, M.[Marco],
Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization,
SSVM23(511-521).
Springer DOI 2307
BibRef

Samele, S.[Stefano], Matteucci, M.[Matteo],
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images,
ICPR22(1307-1313)
IEEE DOI 2212
Representation learning, Location awareness, Surface reconstruction, Dictionaries, Magnetic resonance imaging, Pipelines BibRef

Jang, J.S.[Jin-Seong], Hwang, D.[Dosik],
M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer,
CVPR22(20686-20697)
IEEE DOI 2210
Training, Representation learning, Databases, Open Access, Magnetic resonance imaging, Computer architecture, Medical, Deep learning architectures and techniques BibRef

Cairone, L.[Luca], Benfante, V.[Viviana], Bignardi, S.[Samuel], Marinozzi, F.[Franco], Yezzi, A.[Anthony], Tuttolomondo, A.[Antonino], Salvaggio, G.[Giuseppe], Bini, F.[Fabiano], Comelli, A.[Albert],
Robustness of Radiomics Features to Varying Segmentation Algorithms in Magnetic Resonance Images,
AIRCAD22(462-472).
Springer DOI 2208
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Trigui, R.[Rania], Adel, M.[Mouloud], di Bisceglie, M.[Mathieu], Wojak, J.[Julien], Pinol, J.[Jessica], Faure, A.[Alice], Chaumoitre, K.[Katia],
Comparison of GWO-SVM and Random Forest Classifiers in a LevelSet based approach for Bladder wall segmentation and characterisation using MR images,
IPTA22(1-6)
IEEE DOI 2206
Support vector machines, Measurement, Image segmentation, Shape, Manuals, Bladder, Bladder wall segmentation, Classification, Magnetic Resonance Imaging BibRef

Ding, Z.P.[Zhi-Peng], Han, X.[Xu], Liu, P.R.[Pei-Rong], Niethammer, M.[Marc],
Local Temperature Scaling for Probability Calibration,
ICCV21(6869-6879)
IEEE DOI 2203
Measurement, Temperature distribution, Image segmentation, Impedance matching, Semantics, Magnetic resonance, Segmentation, Scene analysis and understanding BibRef

Ding, H.[Hao], Sun, C.C.[Chang-Chang], Tang, H.[Hao], Cai, D.[Dawen], Yan, Y.[Yan],
Few-shot Medical Image Segmentation with Cycle-resemblance Attention,
WACV23(2487-2496)
IEEE DOI 2302
Semantic segmentation, Computed tomography, Magnetic resonance imaging, Prototypes, Task analysis, visual reasoning) BibRef

Tang, H.[Hao], Liu, X.W.[Xing-Wei], Sun, S.L.[Shan-Lin], Yan, X.Y.[Xiang-Yi], Xie, X.H.[Xiao-Hui],
Recurrent Mask Refinement for Few-Shot Medical Image Segmentation,
ICCV21(3898-3908)
IEEE DOI 2203
Training, Image segmentation, Technological innovation, Correlation, Magnetic resonance imaging, Manuals, Medical, grouping and shape BibRef

Chen, Y.C.[Yung-Chih], Hsieh, J.W.[Jun-Wei], Yang, Y.H.[Yao-Hong], Lee, C.H.[Chien-Hung], Yu, P.Y.[Pei-Yi], Chen, P.Y.[Ping-Yang], Santa, A.S.[Arpita Samanta],
Towards Deep Learning-Based Sarcopenia Screening with Body Joint Composition Analysis,
ICIP21(3807-3811)
IEEE DOI 2201
Muscle degeneration. Training, Muscles, Aging, Real-time systems, Physiology, Clinical diagnosis, Random forests, Sarcopenia classification, LSTM BibRef

Al Suman, A.[Abdulla], Sarda, S.[Shubham], Asikuzzaman, M., Webb, A.L.[Alexandra Louise], Diana, M.P.[M. Perriman], Tahtali, M.[Murat], di Ieva, A.[Antonio], Pickering, M.R.[Mark R.],
Two-stage U-Net++ for Medical Image Segmentation,
DICTA21(01-06)
IEEE DOI 2201
Image segmentation, Magnetic resonance imaging, Digital images, Computer architecture, Muscles, Feature extraction, Neck, U-Net, MRI BibRef

Ma, T.Y.[Tian-Yu], Zhang, H.[Hang], Ong, H.[Hanley], Vora, A.[Amar], Nguyen, T.D.[Thanh D.], Gupta, A.[Ajay], Wang, Y.[Yi], Sabuncu, M.R.[Mert R.],
Ensembling Low Precision Models for Binary Biomedical Image Segmentation,
WACV21(325-334)
IEEE DOI 2106
Image segmentation, Biological system modeling, Magnetic resonance imaging, Predictive models, Brain modeling BibRef

Guo, D.F.[Dan-Feng], Terzopoulos, D.[Demetri],
A Transformer-Based Network for Anisotropic 3D Medical Image Segmentation,
ICPR21(8857-8861)
IEEE DOI 2105
Training, Adaptation models, Solid modeling, Image segmentation, Anisotropic magnetoresistance, Computational modeling BibRef

Kolarik, M.[Martin], Burget, R.[Radim], Travieso-Gonzalez, C.M.[Carlos M.], Kocica, J.[Jan],
Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation,
ICPR21(6051-6058)
IEEE DOI 2105
Training, Image segmentation, Sensitivity, Magnetic resonance imaging, Transfer learning, Training data BibRef

Jurek, J.[Jakub], Reisæter, L.[Lars], Kocinski, M.[Marek], Materka, A.[Andrzej],
On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers: A Simulation Study,
ICCVG20(72-86).
Springer DOI 2009
BibRef

Kiefer, L.[Lukas], Petra, S.[Stefania], Storath, M.[Martin], Weinmann, A.[Andreas],
Direct MRI Segmentation from k-Space Data by Iterative Potts Minimization,
SSVM19(406-418).
Springer DOI 1909
BibRef

Roy, S.[Sudipta], Shoghi, K.I.[Kooresh Isaac],
Computer-Aided Tumor Segmentation from T2-Weighted MR Images of Patient-Derived Tumor Xenografts,
ICIAR19(II:159-171).
Springer DOI 1909
BibRef

Rezaei, M.[Mina], Yang, H.J.[Hao-Jin], Harmuth, K.[Konstantin], Meinel, C.[Christoph],
Conditional Generative Adversarial Refinement Networks for Unbalanced Medical Image Semantic Segmentation,
WACV19(1836-1845)
IEEE DOI 1904
biomedical MRI, brain, computerised tomography, image segmentation, learning (artificial intelligence), medical image processing, Generative adversarial networks BibRef

Basukala, D., Mukundan, R., Melzer, T., Keenan, R.,
Segmentation of Substantia Nigra Using Weighted Thresholding Method,
IVCNZ18(1-6)
IEEE DOI 1902
Image segmentation, Magnetic resonance imaging, Level set, Clustering algorithms, Classification algorithms, Visualization, substantia nigra BibRef

Ghosh, S., Ray, N., Boulanger, P.,
A Structured Deep-Learning Based Approach for the Automated Segmentation of Human Leg Muscle from 3D MRI,
CRV17(117-123)
IEEE DOI 1804
biomedical MRI, convolution, feedforward neural nets, image segmentation, learning (artificial intelligence), principal component analysis (PCA) BibRef

Ghosh, S., Boulanger, P.[Pierre], Acton, S.T., Blemker, S.S., Ray, N.,
Automated 3D Muscle Segmentation from MRI Data Using Convolutional Neural Network,
ICIP17(4437-4441)
IEEE DOI 1803
3D modeling, Magnetic resonance imaging (MRI), convolutional neural networks (CNN), leg muscle segmentation, principal component analysis (PCA) BibRef

Thoma, J.[Janine], Ozdemir, F.[Firat], Goksel, O.[Orcun],
Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker,
MCV16(83-93).
Springer DOI 1711
BibRef

McDonagh, S.[Steven], Hou, B.[Benjamin], Alansary, A.[Amir], Oktay, O.[Ozan], Kamnitsas, K.[Konstantinos], Rutherford, M.[Mary], Hajnal, J.V.[Jo V.], Kainz, B.[Bernhard],
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging,
RAMBO17(116-126).
Springer DOI 1711
BibRef

Tseng, K.L.[Kuan-Lun], Lin, Y.L.[Yen-Liang], Hsu, W.[Winston], Huang, C.Y.[Chung-Yang],
Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation,
CVPR17(3739-3746)
IEEE DOI 1711
Convolution, Decoding, Image segmentation, Tumors, BibRef

Hai, J.J.[Jin-Jin], Chen, J.[Jian], Qiao, K.[Kai], Zeng, L.[Lei], Xu, J.B.[Jing-Bo], Yan, B.[Bin],
Fast medical image segmentation based on patch sharing,
ICIVC17(336-340)
IEEE DOI 1708
Convolution, Image segmentation, Knowledge engineering, Magnetic resonance imaging, Medical diagnostic imaging, convolutional neural network, medical image segmentation, patch sharing BibRef

Fan, B.J.[Bai-Jiang], Rao, Y.[Yunbo], Liu, W.[Wei], Wang, Q.F.[Qi-Fei], Wen, H.Y.[Huai-Yu],
Region-based growing algorithm for 3D reconstruction from MRI images,
ICIVC17(521-525)
IEEE DOI 1708
Image edge detection, Image reconstruction, Image segmentation, Magnetic resonance imaging, Mathematical model, Solid modeling, 3D reconstruction, image segmentation, multiple angle observation, region-based, growing BibRef

Mohebpour, M.R., Guibault, F., Cheriet, F.,
Mesh-Based Active Model Initialization for Multiple Organ Segmentation in MR Images,
ICIAR17(429-436).
Springer DOI 1706
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Christensen, A.N.[Anders Nymark], Larsen, C.T.[Christian Thode], Mandrup, C.M.[Camilla Maria], Petersen, M.B.[Martin Bæk], Larsen, R.[Rasmus], Conradsen, K.[Knut], Dahl, V.A.[Vedrana Andersen],
Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies,
SCIA17(II: 109-120).
Springer DOI 1706
BibRef

Lapuyade-Lahorgue, J., Ruan, S., Li, H., Vera, P.,
Tumor segmentation by fusion of MRI images using copula based statistical methods,
ICIP16(4136-4139)
IEEE DOI 1610
Hidden Markov models BibRef

Cardona, H.D.V.[Hernán Darío Vargas], López-Lopera, A.F.[Andrés F.], Orozco, Á.A.[Álvaro A.], Álvarez, M.A.[Mauricio A.], Tamames, J.A.H.[Juan Antonio Hernández], Malpica, N.[Norberto],
Gaussian Processes for Slice-Based Super-Resolution MR Images,
ISVC15(II: 692-701).
Springer DOI 1601
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Pan, X.[Xu], Zhu, H.Q.[Hong-Qing], Xie, Q.Y.[Qun-Yi],
A robust nonsymmetric student's-t finite mixture model for MR image segmentation,
ICIP15(1830-1834)
IEEE DOI 1512
MR image BibRef

Ito, S.[Satoshi], Yasaka, S.[Shungo], Yamada, Y.[Yoshifumi],
MR image reconstruction of a regularly undersampled signal using quadratic phase scrambling,
ICIP15(2994-2998)
IEEE DOI 1512
Fresnel transform; aliasing; compressed sensing; sampling BibRef

Adhikari, S.K., Sing, J.K., Basu, D.K., Nasipuri, M.,
A spatial fuzzy C-means algorithm with application to MRI image segmentation,
ICAPR15(1-6)
IEEE DOI 1511
biomedical MRI BibRef

Aparajeeta, J., Nanda, P.K., Das, N.,
Bias field estimation and segmentation of MR image using modified fuzzy-C means algorithms,
ICAPR15(1-6)
IEEE DOI 1511
biomedical MRI BibRef

Roy, A., Maity, S.P.,
On segmentation of CS reconstructed MR images,
ICAPR15(1-6)
IEEE DOI 1511
adaptive filters BibRef

Orbes-Arteaga, M.[Mauricio], Cárdenas-Peña, D.[David], Álvarez, M.A.[Mauricio A.], Orozco, A.A.[Alvaro A.], Castellanos-Dominguez, G.[Germán],
Magnetic Resonance Image Selection for Multi-Atlas Segmentation Using Mixture Models,
CIARP15(391-399).
Springer DOI 1511
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Khatami, M.[Mohammad], Schmidt-Wilcke, T.[Tobias], Sundgren, P.C.[Pia C.], Abbasloo, A.[Amin], Schölkopf, B.[Bernhard], Schultz, T.[Thomas],
BundleMAP: Anatomically localized classification, regression, and hypothesis testing in diffusion MRI,
PR(63), No. 1, 2017, pp. 593-600.
Elsevier DOI 1612
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BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease,
MLMI15(52-60).
Springer DOI 1511
Disease detection BibRef

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Unsupervised muscle region extraction by fuzzy decision based saliency feature integration on thigh MRI for 3D modeling,
MVA15(150-153)
IEEE DOI 1507
Feature extraction BibRef

Cárdenas-Peña, D.[David], Orozco, A.A.[Alvaro A.], Castellanos-Dominguez, G.[Germán],
Information-Based Cost Function for a Bayesian MRI Segmentation Framework,
CIAP15(I:548-556).
Springer DOI 1511
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Orbes-Arteaga, M.[Mauricio], Cárdenas-Peña, D.[David], Álvarez, M.A.[Mauricio A.], Orozco, A.A.[Alvaro A.], Castellanos-Dominguez, G.[Germán],
Kernel Centered Alignment Supervised Metric for Multi-Atlas Segmentation,
CIAP15(I:658-667).
Springer DOI 1511
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Earlier:
Spatial-Dependent Similarity Metric Supporting Multi-atlas MRI Segmentation,
IbPRIA15(300-308).
Springer DOI 1506
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Lin, E.U.[En-Ui], McLaughlin, M., Alshehri, A.A., Ezekiel, S., Farag, W.,
Medical image segmentation using multi-scale and super-resolution method,
AIPR14(1-5)
IEEE DOI 1504
biomedical MRI BibRef

Trebuchet, G., Fasquel, J., Cavaro-Menard, C., Willoteaux, S.,
Coupling anatomical and functional information for the computer-aided delineation of Phase-Contrast MRI images using active contours,
IPTA12(172-177)
IEEE DOI 1503
biomedical MRI BibRef

Antony, J., McGuinness, K., Welch, N., Coyle, J., Franklyn-Miller, A., OConnor, N.E., Moran, K.,
Fat quantification in MRI-defined lumbar muscles,
IPTA14(1-6)
IEEE DOI 1503
biomedical MRI BibRef

Kinani, J.M.V.[J.M. Vianney], Rosales-Silva, A.J., Gallegos-Funes, F.J., Arellano, A.,
Fuzzy C-means applied to MRI images for an automatic lesion detection using image enhancement and constrained clustering,
IPTA14(1-7)
IEEE DOI 1503
biomedical MRI BibRef

Pham, M.H., Doncescu, A.,
Detection of the features of the objects in MR images using dynamic programming,
IPTA14(1-6)
IEEE DOI 1503
biomedical MRI BibRef

Benkarim, O.M.[Oualid M.], Radeva, P.I.[Petia I.], Igual, L.[Laura],
Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation,
AMDO14(138-147).
Springer DOI 1407
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Mazo, C.[Claudia], Trujillo, M.[Maria], Salazar, L.[Liliana],
Identifying Loose Connective and Muscle Tissues on Histology Images,
CIARP13(II:174-180).
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Ivanovska, T.[Tatyana], Laqua, R.[René], Wang, L.[Lei], Völzke, H.[Henry], Hegenscheid, K.[Katrin],
Fast Implementations of the Levelset Segmentation Method With Bias Field Correction in MR Images: Full Domain and Mask-Based Versions,
IbPRIA13(674-681).
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Purushwalkam, S.[Senthil], Li, B.H.[Bai-Hua], Meng, Q.G.[Qing-Gang], McPhee, J.[Jamie],
Automatic Segmentation of Adipose Tissue from Thigh Magnetic Resonance Images,
ICIAR13(451-458).
Springer DOI 1307
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Selvathi, D., Dhivya, R.,
Segmentation of tissues in MR images using Modified Spatial Fuzzy C Means algorithm,
ICSIPR13(136-140).
IEEE DOI 1304
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ICIP12(2037-2040).
IEEE DOI 1302
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Baudin, P.Y.[Pierre-Yves], Azzabou, N.[Noura], Carlier, P.[Pierre], Paragios, N.[Nikos],
Manifold-enhanced Segmentation through Random Walks on Linear Subspace Priors,
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Salehian, H.[Hesamoddin], Cheng, G.[Guang], Vemuri, B.C.[Baba C.], Ho, J.[Jeffrey],
Recursive Estimation of the Stein Center of SPD Matrices and Its Applications,
ICCV13(1793-1800)
IEEE DOI 1403
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Wang, Y.X.[Yuan-Xiang], Salehian, H.[Hesamoddin], Cheng, G.[Guang], Vemuri, B.C.[Baba C.],
Tracking on the Product Manifold of Shape and Orientation for Tractography from Diffusion MRI,
CVPR14(3051-3056)
IEEE DOI 1409
Riemannian Manifold;Tractography;Unscented Kalman Filter BibRef

Cheng, G.[Guang], Salehian, H.[Hesamoddin], Vemuri, B.C.[Baba C.],
Efficient Recursive Algorithms for Computing the Mean Diffusion Tensor and Applications to DTI Segmentation,
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Liu, Z.[Zheng], Nutter, B.[Brian], Mitra, S.[Sunanda],
Compressive sampling in fast wavelet-encoded MRI,
Southwest12(137-140).
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A geometric analysis of ODFs as oriented surfaces for interpolation, averaging and denoising in HARDI data,
MMBIA12(1-6).
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Singh, V.[Vimal], Wang, D.[Dan], Tewfik, A.H.[Ahmed H.],
Segmented rapid magnetic resonance imaging using structured sparse representations,
ICIP11(2277-2260).
IEEE DOI 1201
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Yaqub, M.[Mohammad], Javaid, M.K.[M. Kassim], Cooper, C.[Cyrus], Noble, J.A.[J. Alison],
Improving the Classification Accuracy of the Classic RF Method by Intelligent Feature Selection and Weighted Voting of Trees with Application to Medical Image Segmentation,
MLMI11(184-192).
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3D MRI Segmentation. BibRef

Zhou, J.[Jiayin], Tian, Q.[Qi], Chong, V.[Vincent], Xiong, W.[Wei], Huang, W.M.[Wei-Min], Wang, Z.M.[Zhi-Min],
Segmentation of Skull Base Tumors from MRI Using a Hybrid Support Vector Machine-Based Method,
MLMI11(134-141).
Springer DOI 1109
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Farzinfar, M., Teoh, E.K.[Eam Khwang], Xue, Z.[Zhong],
A coupled implicit shape-based deformable model for segmentation of MR images,
ICARCV08(651-656).
IEEE DOI 1109
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Tran, T.T., Lee, P.L.[Po-Lei], Pham, V.T., Shyu, K.K.[Kuo-Kai],
MRI image segmentation based on fast global minimization of snake model,
ICARCV08(1769-1772).
IEEE DOI 1109
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Mosbech, T.H.[Thomas Hammershaimb], Pilgaard, K.[Kasper], Vaag, A.[Allan], Larsen, R.[Rasmus],
Automatic Segmentation of Abdominal Adipose Tissue in MRI,
SCIA11(501-511).
Springer DOI 1105
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Feltell, D.[David], Bai, L.[Li],
A New Marching Cubes Algorithm for Interactive Level Set with Application to MR Image Segmentation,
ISVC10(I: 371-380).
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Koh, J.[Jaehan], Chaudhary, V.[Vipin], Dhillon, G.[Gurmeet],
A fully automated method of associating axial slices with a disc based on labeling of multi-protocol lumbar MRI,
ICIP10(4341-4344).
IEEE DOI 1009
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Turnes, C.K.[Christopher K.], Romberg, J.[Justin],
Spiral FFT: An efficient method for 3-D FFTS on spiral MRI contours,
ICIP10(617-620).
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Khider, M.[Mohamed], Taleb-Ahmed, A.[Abdelmalik], Haddad, B.[Boualem],
Generation of Synthetic Multifractal Realistic Surfaces Based on Natural Model and Lognormal Cascade: Application to MRI Classification,
CIARP10(71-78).
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Donoso, R.[Ramiro], Veloz, A.[Alejandro], Allende, H.[Héctor],
Modified Expectation Maximization Algorithm for MRI Segmentation,
CIARP10(63-70).
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Li, Y.[Yi], Gao, Z.J.[Zhi-Jun],
A review of segmentation method for MR image,
IASP10(351-357).
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Ray, D.[Dipankar], Majumder, D.D.[D. Dutta],
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SCIA09(179-188).
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Leinhard, O.D.[O. Dahlqvist], Johansson, A., Rydell, J., Smedby, O., Nystrom, F., Lundberg, P., Borga, M.,
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ICPR08(1-4).
IEEE DOI 0812
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Faggian, N., Chen, Z.L.[Zhao-Lin], Johnston, L., Oh, S.H.[Se-Hong], Cho, Z.H.[Zang-Hee], Egan, G.,
A Method for Shape Analysis and Segmentation in MRI,
DICTA08(335-342).
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Goh, A.[Alvina], Lenglet, C.[Christophe], Thompson, P.M.[Paul M.], Vidal, R.[Rene],
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CVPR09(2496-2503).
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MRI data. BibRef

Li, C.M.[Chun-Ming], Gatenby, C.[Chris], Wang, L.[Li], Gore, J.C.[John C.],
A Robust Parametric Method for Bias Field Estimation and Segmentation of MR Images,
CVPR09(218-223).
IEEE DOI 0906
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Tang, S.Y.[Song-Yuan], Fan, Y.[Yong], Zhu, H.T.[Hong-Tu], Yap, P.T.[Pew-Thian], Gao, W.[Wei], Lin, W.L.[Wei-Li], Shen, D.G.[Ding-Gang],
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MMBIA09(52-57).
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Jodoin, P.M.[Pierre-Marc], Lalande, A.[Alain], Voisin, Y.[Yvon], Bouchot, O.[Olivier], Steinmetz, E.[Eric],
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Babacan, S.D.[S. Derin], Yin, X.M.[Xiao-Ming], Larson, A.C.[Andrew C.], Katsaggelos, A.K.[Aggelos K.],
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Placidi, G.[Giuseppe],
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CompIMAGE10(254-262).
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ISVC08(II: 1115-1124).
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Wong, A.[Alexander],
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Schultz, T.[Thomas], Seidel, H.P.[Hans-Peter],
Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data,
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Mayer, G.S., Vrscay, E.R.[Edward R.], Lauzon, M.L., Goodyear, B.G., Mitchell, J.R.,
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ICIP07(III: 133-136).
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Image Analysis of Renal DCE MRI for the Detection of Acute Renal Rejection,
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IEEE DOI 0609
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Balov, N.[Nikolay], Srivastava, A.[Anuj], Li, C.M.[Chun-Ming], Ding, Z.H.[Zhao-Hua],
Shape Analysis of Open Curves in R3 with Applications to Study of Fiber Tracts in DT-MRI Data,
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Danyali, H.[Habibollah], Mertins, A.[Alfred],
Multiresolution Lossy-to-Lossless Coding of MRI Objects,
ACIVS06(877-886).
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Cao, Y.[Yan], Miller, M.I.[Michael I.], Mori, S.[Susumu], Winslow, R.L.[Raimond L.], Younes, L.[Laurent],
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MMBIA06(67).
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Balci, M.[Murat], Alnasser, M.[Mais], Foroosh, H.[Hassan],
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ICPR06(III: 607-610).
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McGraw, T.[Tim], Vemuri, B.C.[Baba C.], Yezierski, R.[Robert], Mareci, T.[Thomas],
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Bronstein, A.M., Bronstein, M.M., Zibulevsky, M., Zeevi, Y.Y.,
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ICIP05(II: 1282-1285).
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Persson, M.[Markus], Solem, J.E.[Jan Erik], Markenroth, K.[Karin], Svensson, J.[Jonas], Heyden, A.[Anders],
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ScaleSpace05(119-130).
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Chen, W.F.[Wu-Fan], Zhou, S.J.[Shou-Jun], Liang, B.[Bin],
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ICIP04(I: 373-376).
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Minagawa, A., Takahashi, S., Tagawa, N.,
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ICIP03(I: 1073-1076).
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Hellier, P.,
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ICIP03(I: 1109-1112).
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Ardizzone, E.[Edoardo], Pirrone, R.[Roberto], Gambino, O.[Orazio],
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CIAP03(283-288).
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Magnetic Resonance Imaging, Registration, Alignment, Fusion .