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Zhang, X.,
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Earlier:
The shading zone problem in geodesic voting and its solutions for the
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Earlier:
Image segmentation by geodesic voting. Application to the extraction of
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Geodesic voting
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Magnetic resonance imaging (MRI)
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fuzzy entropy, Magnetic Resonance Image, Fuzzy Membership function
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IEEE DOI
1302
biomedical MRI
BibRef
Jafarian, N.[Nassim],
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Springer DOI
1402
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1403
biomedical MRI
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Kim, Y.,
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1405
Blood oxygen measurements
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MR image
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1406
Image segmentation
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Springer DOI
1407
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Oguz, I.,
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LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple
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IEEE DOI
1407
Image reconstruction
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Kong, Y.,
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1411
Educational institutions
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Comparative Evaluation of Registration Algorithms in Different Brain
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1411
biomedical MRI
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fuzzy entropy clustering
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1506
biomedical MRI
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1507
Magnetic resonance imaging
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magnetic resonance imaging (MRI)
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Chung, M.K.,
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Persistent Homology in Sparse Regression and Its Application to Brain
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1509
Brain modeling
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Becker, H.,
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1511
Biomedical signal processing
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1512
magnetic resonance imaging
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1605
Biomedical image processing
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Agnello, L.[Luca],
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1606
voxel-based morphometry
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multivariate student t-distribution for brain MRI segmentation,
PR(60), No. 1, 2016, pp. 778-792.
Elsevier DOI
1609
Anisotropic spatial information
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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
BibRef
Earlier: A1, A2, A4, Only:
Discrete Multi Atlas Segmentation using Agreement Constraints,
BMVC14(xx-yy).
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1410
BibRef
Demirhan, A.[Ayse],
Neuroimage-based clinical prediction using machine learning tools,
IJIST(27), No. 1, 2017, pp. 89-97.
DOI Link
1704
structural brain MR images
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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],
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operations, texture and set analysis,
IET-IPR(11), No. 9, September 2017, pp. 672-684.
DOI Link
1709
BibRef
Namburu, A.[Anupama],
Samayamantula, S.K.[Srinivas Kumar],
Edara, S.R.[Srinivasa Reddy],
Generalised rough intuitionistic fuzzy c-means for magnetic resonance
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IET-IPR(11), No. 9, September 2017, pp. 777-785.
DOI Link
1709
BibRef
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
BibRef
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.
DOI Link
1804
BibRef
Rodríguez-Domínguez, U.[Ulises],
Dalmau, O.[Oscar],
Bosch-Bayard, J.[Jorge],
Atlas-based segmentation of neonatal brain MR images using a gray
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SIViP(12), No. 4, May 2018, pp. 633-640.
Springer DOI
1805
BibRef
El-Dahshan, E.S.A.[El-Sayed A.],
Bassiouni, M.M.[Mahmoud M.],
Computational intelligence techniques for human brain MRI
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IJIST(28), No. 2, 2018, pp. 132-148.
WWW Link.
1806
BibRef
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.
DOI Link
1807
BibRef
Liu, Y.L.[Yi-Lin],
Du, H.Q.[Hui-Qian],
Wang, Z.X.[Ze-Xian],
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Convex MR brain image reconstruction via non-convex total variation
minimization,
IJIST(28), No. 4, December 2018, pp. 246-253.
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1811
BibRef
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,
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BibRef
Saboori, A.[Arash],
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PET-MRI image fusion using adaptive filter based on spectral and
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SIViP(13), No. 1, February 2019, pp. 135-143.
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1901
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Balachandrasekaran, A.,
Mani, M.,
Jacob, M.,
Calibration-Free B0 Correction of EPI Data Using Structured Low Rank
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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
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SIIMS(12), No. 1, 2019, pp. 492-520.
DOI Link
1904
BibRef
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
BibRef
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.
DOI Link
1908
BibRef
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
Earlier:
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],
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Fuzzy farthest point first method for MRI brain image clustering,
IET-IPR(13), No. 13, November 2019, pp. 2395-2400.
DOI Link
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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
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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
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SPLetters(27), 2020, pp. 141-145.
IEEE DOI
2001
Convolutional neural network (CNN), image reconstruction,
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BibRef
Zamani, H.,
Razavikia, S.,
Otroshi-Shahreza, H.,
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Separation of Nonlinearly Mixed Sources Using End-to-End Deep Neural
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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
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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
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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
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IET-IPR(14), No. 9, 20 July 2020, pp. 1929-1936.
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Pham, T.X.,
Siarry, P.,
Oulhadj, H.,
Segmentation of MR Brain Images Through Hidden Markov Random Field
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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],
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Automatic labelling of brain tissues in MR images through spatial
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IET-IPR(14), No. 12, October 2020, pp. 2728-2736.
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BibRef
Nayak, D.R.[Deepak Ranjan],
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Automated diagnosis of multi-class brain abnormalities using MRI
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PRL(138), 2020, pp. 385-391.
Elsevier DOI
1806
Magnetic resonance imaging, Deep learning, Brain disease, CNN, Transfer learning
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Lu, S.Y.[Si-Yuan],
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A classification method for brain MRI via MobileNet and feedforward
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Elsevier DOI
2012
Computer aided diagnosis, Magnetic resonance image, MobileNet,
Extreme learning machine, Random vector functional-link net,
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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, 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
BibRef
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
Wang, X.L.[Xiang-Long],
Rigall, E.[Eric],
An, X.F.[Xi-Feng],
Li, Z.H.[Zhi-Hao],
Cai, Q.[Qing],
Zhang, S.[Shu],
Dong, J.Y.[Jun-Yu],
A New Benchmark and Low Computational Cost Localization Method for
Cephalometric Analysis,
CirSysVideo(34), No. 9, September 2024, pp. 8939-8952.
IEEE DOI Code:
WWW Link.
2410
Location awareness, Heating systems, Computational modeling,
Computational efficiency, Decoding, Quantization (signal), heatmaps decoding
BibRef
Billot, B.[Benjamin],
Dey, N.[Neel],
Moyer, D.[Daniel],
Hoffmann, M.[Malte],
Turk, E.A.[Esra Abaci],
Gagoski, B.[Borjan],
Grant, P.E.[P. Ellen],
Golland, P.[Polina],
SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in
Brain MRI,
MedImg(43), No. 11, November 2024, pp. 4029-4040.
IEEE DOI Code:
WWW Link.
2411
Tracking, Magnetic resonance imaging, Feature extraction,
Transforms, Convolution, Biomedical imaging, fetal MRI
BibRef
Vikraman, B.P.[Bindu Puthentharayil],
Afthab, J.[Jabeena],
Effective image compression using hybrid DCT and hybrid capsule auto
encoder for brain MR images,
JVCIR(104), 2024, pp. 104296.
Elsevier DOI
2411
Image compression, Discrete cosine transform-improved zero wavelet,
Capsule auto encoder
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
Rasal, R.[Rajat],
Castro, D.C.[Daniel C.],
Pawlowski, N.[Nick],
Glocker, B.[Ben],
Deep Structural Causal Shape Models,
CiV22(400-432).
Springer DOI
2304
Mesh descriptions of brain structures.
BibRef
Wen, Y.F.[Yu-Fei],
Liang, C.X.[Chong-Xin],
Lin, J.Y.[Jing-Yin],
Wu, H.[Huisi],
Qin, J.[Jing],
Exswin-unet: An Unbalanced Weighted Unet with Shifted Window and
External Attentions for Fetal Brain MRI Image Segmentation,
MCV22(340-354).
Springer DOI
2304
BibRef
Specktor-Fadida, B.[Bella],
Yehuda, B.[Bossmat],
Link-Sourani, D.[Daphna],
Ben-Sira, L.[Liat],
Ben-Bashat, D.[Dafna],
Joskowicz, L.[Leo],
Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in
Fetal MRI,
MCV22(355-368).
Springer DOI
2304
BibRef
Yang, Y.[Yanwu],
Guo, X.[Xutao],
Chang, Z.K.[Zhi-Kai],
Ye, C.F.[Chen-Fei],
Xiang, Y.[Yang],
Lv, H.Y.[Hai-Yan],
Ma, T.[Ting],
Estimating Brain Age with Global and Local Dependencies,
ICIP22(56-60)
IEEE DOI
2211
Deep learning, Image coding, Convolution,
Magnetic resonance imaging, Transformers, CNN
BibRef
Bongratz, F.[Fabian],
Rickmann, A.M.[Anne-Marie],
Pölsterl, S.[Sebastian],
Wachinger, C.[Christian],
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D
MRI Scans with Geometric Deep Neural Networks,
CVPR22(20741-20751)
IEEE DOI
2210
Geometry, Deep learning, Surface reconstruction,
Magnetic resonance imaging, Surface morphology, grouping and shape analysis
BibRef
Perlo, D.[Daniele],
Tartaglione, E.[Enzo],
Gava, U.[Umberto],
d'Agata, F.[Federico],
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Bergui, M.[Mauro],
UniToBrain Dataset: A Brain Perfusion Dataset,
DeepHealth22(498-509).
Springer DOI
2208
BibRef
Ghaffari, M.[Mina],
Pawar, K.[Kamlesh],
Oliver, R.[Ruth],
Brain MRI motion artifact reduction using 3D conditional generative
adversarial networks on simulated motion,
DICTA21(1-7)
IEEE DOI
2201
Training, Solid modeling, PSNR, Smoothing methods,
Magnetic resonance imaging, Brain modeling, brain MRI,
conditional Generative adversarial networks
BibRef
Ravindra, V.[Vikram],
Sanders, G.[Geoffrey],
Grama, A.[Ananth],
Identifying Coherent Subgraphs In Dynamic Brain Networks,
ICIP21(121-125)
IEEE DOI
2201
Knowledge engineering, Analytical models, Correlation,
Magnetic resonance imaging, Image processing, Time series analysis
BibRef
Zhu, Y.P.[Yong-Pei],
Zhou, Z.C.[Zi-Cong],
Liao, G.J.[Guo-Jun],
Yuan, K.H.[Ke-Hong],
BCAU-Net: A Novel Architecture with Binary Channel Attention Module
for MRI Brain Segmentation,
ICPR21(5690-5695)
IEEE DOI
2105
Image segmentation, Image coding, Magnetic resonance imaging,
Aggregates, Magnetic resonance, Feature extraction, Brain modeling,
Spatial Pyramid Pooling
BibRef
He, Z.B.[Zhi-Bin],
Huang, Y.[Ying],
Liu, T.M.[Tian-Ming],
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Zhang, T.[Tuo],
Species-preserved Structural Connections Revealed by Sparse Tensor CCA,
MBIA19(49-56).
Springer DOI
1912
For studies of evolution.
BibRef
Gupta, K.,
Awate, S.P.,
Bayesian Reconstruction of Undersampled Multicoil HARDI,
ICIP19(1247-1251)
IEEE DOI
1910
Reconstruction, HARDI, parallel imaging, undersampling, dictionary,
Rician noise, Bayesian
BibRef
Chen, K.Y.[Kai-Yuan],
Shen, J.Y.[Jing-Yue],
Scalzo, F.[Fabien],
Skull Stripping Using Confidence Segmentation Convolution Neural
Network,
ISVC18(15-24).
Springer DOI
1811
BibRef
Duan, Y.,
Fan, X.,
Cheng, H.,
Kang, H.,
Gradient Regression for Brain Landmark Localization on Magnetic
Resonance Imaging,
ICIP18(4013-4017)
IEEE DOI
1809
Shape, Training, Magnetic resonance imaging, gradient regression.
BibRef
Karkouri, J.,
Millioz, F.,
Viallon, M.,
Prost, R.,
Ratiney, H.,
Time samples selection in spiral acquisition for sparse magnetic
resonance spectroscopic imaging,
ICIP17(4128-4131)
IEEE DOI
1803
biomedical MRI, brain, diseases, image sampling,
least squares approximations, medical image processing,
under-sampling
BibRef
Mi, L.,
Zhang, W.,
Zhang, J.,
Fan, Y.,
Goradia, D.,
Chen, K.,
Reiman, E.M.,
Gu, X.,
Wang, Y.,
An Optimal Transportation Based Univariate Neuroimaging Index,
ICCV17(182-191)
IEEE DOI
1802
biomedical MRI, brain, cognition, diseases, medical image processing,
neurophysiology, positron emission tomography,
Transportation
BibRef
Hajiesmaeili, M.,
Amirfakhrian, M.,
A new approach to locate the hippocampus nest in brain MR images,
IPRIA17(140-145)
IEEE DOI
1712
biomedical MRI, brain, diseases, estimation theory,
image segmentation, medical image processing,
skull stripping
BibRef
Nguyen, D.M.H.[Duy M. H.],
Vu, H.T.[Huy T.],
Ung, H.Q.[Huy Q.],
Nguyen, B.T.[Binh T.],
3D-Brain Segmentation Using Deep Neural Network and Gaussian Mixture
Model,
WACV17(815-824)
IEEE DOI
1609
Biological neural networks, Brain modeling, Image segmentation,
Magnetic resonance imaging, Testing, Three-dimensional, displays
BibRef
Damseh, R.[Rafat],
Ahmad, M.O.[M. Omair],
Curvelet-Based Classification of Brain MRI Images,
ICIAR17(446-454).
Springer DOI
1706
BibRef
Al-Dmour, H.[Hayat],
Al-Ani, A.[Ahmed],
MR Brain Tissue Segmentation Based on Clustering Techniques and Neural
Network,
CIAP17(II:225-233).
Springer DOI
1711
BibRef
Earlier:
MR Brain Image Segmentation Based on Unsupervised and Semi-Supervised
Fuzzy Clustering Methods,
DICTA16(1-7)
IEEE DOI
1701
Biomedical imaging
BibRef
Ferrari, R.J.,
Pinto, C.H.V.,
Moreira, C.A.F.,
Detection of the midsagittal plane in MR images using a sheetness
measure from eigenanalysis of local 3D phase congruency responses,
ICIP16(2335-2339)
IEEE DOI
1610
Brain
BibRef
González-Castro, V.[Víctor],
Valdés Hernández, M.D.C.[María Del C.],
Armitage, P.A.[Paul A.],
Wardlaw, J.M.[Joanna M.],
Automatic Rating of Perivascular Spaces in Brain MRI Using Bag of
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ICIAR16(642-649).
Springer DOI
1608
BibRef
Ismail, M.,
Mostapha, M.,
Soliman, A.,
Nitzken, M.,
Khalifa, F.,
Elnakib, A.,
Gimel'farb, G.,
Casanova, M.F.,
El-Baz, A.,
Segmentation of infant brain MR images based on adaptive shape prior
and higher-order MGRF,
ICIP15(4327-4331)
IEEE DOI
1512
Adaptive Shape; Higher Order MGRF; Infant Brain Segmentation
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Bai, X.Z.[Xiang-Zhi],
Chen, Z.G.[Zhi-Guo],
Liu, M.[Miaoming],
Zhang, Y.[Yu],
Center-free PFCM for MRI brain image segmentation,
ICIP15(656-660)
IEEE DOI
1512
Center-Free
BibRef
Mostapha, M.[Mahmoud],
Casanova, M.F.[Manuel F.],
El-Baz, A.[Ayman],
A novel framework for the segmentationof mrinfant brain images,
ICIP15(88-92)
IEEE DOI
1512
DTI; Infant; Subject-specific atlas; Tissue segmentation
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Roy, S.,
Maji, P.,
A simple skull stripping algorithm for brain MRI,
ICAPR15(1-6)
IEEE DOI
1511
biomedical MRI
BibRef
Dubey, Y.K.,
Mushrif, M.M.,
Intuitionistic fuzzy roughness measure for segmentation of brain MR
images,
ICAPR15(1-6)
IEEE DOI
1511
brain
BibRef
Phophalia, A.[Ashish],
Mitra, S.K.[Suman K.],
Rician Noise Removal Approach for Brain MR Images Using Kernel
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PReMI15(545-553).
Springer DOI
1511
BibRef
Minervini, M.,
Damiano, M.,
Tucci, V.,
Bifone, A.,
Gozzi, A.,
Tsaftaris, S.A.,
Mouse neuroimaging phenotyping in the cloud,
IPTA12(55-60)
IEEE DOI
1503
biomedical MRI
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Xu, N.[Nan],
Spreng, R.N.[R.Nathan],
Doerschuk, P.C.[Peter C.],
Directed interactivity of large-scale brain networks: Introducing a
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ICIP14(3508-3512)
IEEE DOI
1502
Computational modeling
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Nataraj, G.[Gopal],
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Model-based estimation of T2 maps with dual-echo steady-state MR
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ICIP14(1877-1881)
IEEE DOI
1502
Accuracy
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Sjolund, J.[Jens],
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Skull Segmentation in MRI by a Support Vector Machine Combining Local
and Global Features,
ICPR14(3274-3279)
IEEE DOI
1412
Bones
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Cardenas-Pena, D.,
Orbes-Arteaga, M.,
Castro-Ospina, A.,
Alvarez-Meza, A.,
Castellanos-Dominguez, G.,
A Kernel-Based Representation to Support 3D MRI Unsupervised
Clustering,
ICPR14(3203-3208)
IEEE DOI
1412
Brain modeling
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Kernel-Based Image Representation for Brain MRI Discrimination,
CIARP14(343-350).
Springer DOI
1411
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Roy, S.[Snehashis],
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Pham, D.L.[Dzung L.],
Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI
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MLMI14(248-255).
Springer DOI
1410
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Ma, G.K.[Guang-Kai],
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Soft-Split Random Forest for Anatomy Labeling,
MLMI15(17-25).
Springer DOI
1511
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Ma, G.K.[Guang-Kai],
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Atlas-Guided Multi-channel Forest Learning for Human Brain Labeling,
MCV14(97-104).
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1501
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Dynamic Tree-Based Large-Deformation Image Registration for Multi-atlas
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MCV15(137-145).
Springer DOI
1608
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Zhang, L.[Lichi],
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Learning of Atlas Forest Hierarchy for Automatic Labeling of MR Brain
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MLMI14(323-330).
Springer DOI
1410
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Ge, H.K.[Hong-Kun],
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Hierarchical Multi-modal Image Registration by Learning Common Feature
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MLMI15(203-211).
Springer DOI
1511
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Phellan, R.[Renzo],
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Udupa, J.K.[Jayaram K.],
Improving Atlas-Based Medical Image Segmentation with a Relaxed Object
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CompIMAGE14(152-163).
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1407
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Chan, S.L.S.,
Gal, Y.,
Jeffree, R.L.,
Fay, M.,
Thomas, P.,
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Automated Classification of Bone and Air Volumes for Hybrid PET-MRI
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DICTA13(1-8)
IEEE DOI
1402
biomedical MRI
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Mogali, J.K.[Jayanth Krishna],
Nallapareddy, N.[Naren],
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A shape-template based two-stage corpus callosum segmentation
technique for sagittal plane T1-weighted brain magnetic resonance
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ICIP13(1177-1181)
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1402
Biomedical imaging
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Miranda, P.A.V.[Paulo A.V.],
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A case analysis of the impact of prior center of gravity estimation
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ICIP13(675-679)
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1402
Biomedical imaging
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Tohka, J.[Jussi],
FAST-PVE: Extremely Fast Markov Random Field Based Brain MRI Tissue
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SCIA13(266-276).
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1311
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Bayesian Approach for Reconstruction of Moving Brain Dipoles,
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1307
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Kuijf, H.J.[Hugo J.],
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Automatic Extraction of the Curved Midsagittal Brain Surface on MR
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MCVM12(225-232).
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1305
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Zhang, Z.P.[Zhan-Peng],
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ICPR12(89-92).
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ICIP12(1265-1268).
IEEE DOI
1302
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Wang, J.Q.[Jie-Qiong],
Dai, D.[Dai],
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Hua, J.[Jing],
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Human Age Estimation with Surface-Based Features from MRI Images,
MLMI12(111-118).
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1211
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Landman, B.A.[Bennett A.],
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Do We Really Need Robust and Alternative Inference Methods for Brain
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MBIA12(77-93).
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1210
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Cao, F.[Fang],
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MRI Estimation of T1 Relaxation Time Using a Constrained Optimization
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MBIA12(203-214).
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1210
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Silva, S.[Samuel],
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Segmentation and Analysis of Vocal Tract from MidSagittal Real-Time MRI,
ICIAR13(459-466).
Springer DOI
1307
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Silva, S.[Samuel],
Martins, P.[Paula],
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Silva, A.[Augusto],
Teixeira, A.[António],
Segmentation and Analysis of the Oral and Nasal Cavities from MR Time
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ICIAR12(II: 214-221).
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1206
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Fazlollahi, A.[Amir],
Meriaudeau, F.[Fabrice],
Villemagne, V.L.[Victor L.],
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Yates, P.A.[Paul A.],
Salvado, O.[Olivier],
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Automatic detection of small spherical lesions using multiscale
approach in 3D medical images,
ICIP13(1158-1162)
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1402
Biomedical imaging
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Wang, J.[Jiabin],
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Feng, D.D.,
Differential Evolution Based Variational Bayes Inference for Brain
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DICTA11(330-334).
IEEE DOI
1205
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Kurkure, U.[Uday],
Le, Y.H.[Yen H.],
Paragios, N.[Nikos],
Ju, T.[Tao],
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Kakadiaris, I.A.[Ioannis A.],
Markov Random Field-based fitting of a subdivision-based geometric
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ICCV11(2540-2547).
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1201
Brain structure.
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Gorthi, S.[Subrahmanyam],
Thiran, J.P.[Jean-Philippe],
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ICIP11(57-60).
IEEE DOI
1201
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Elnakib, A.,
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ICPR12(41-44).
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Nitzken, M.,
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3D shape analysis of the brain cortex with application to dyslexia,
ICIP11(2657-2660).
IEEE DOI
1201
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Ramaiah, N.P.[N. Pattabhi],
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ROI-based tissue type extraction and volume estimation in 3D brain
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ICIIP11(1-5).
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1112
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Shanbhag, S.S.,
Udupi, G.R.,
Patil, K.M.,
Ranganath, K.,
Analysis of brain MRI images of intracerebral haemorrhage using
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IEEE DOI
1112
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Farzinfar, M.,
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Applying training hidden features to joint curve evolution for brain
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1109
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Caldairou, B.[Benoît],
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Data-Driven Cortex Segmentation in Reconstructed Fetal MRI by Using
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CAIP11(I: 503-511).
Springer DOI
1109
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Rajalingham, R.[Rishi],
Toews, M.[Matthew],
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Arbel, T.[Tal],
Exploring Cortical Folding Pattern Variability Using Local Image
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MCV10(43-53).
Springer DOI
1009
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Boucher, M.[Maxime],
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A Texture Manifold for Curve-Based Morphometry of the Cerebral Cortex,
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Springer DOI
1009
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Farag, A.[Ahmed],
Elhabian, S.Y.[Shireen Y.],
Abdelrahman, M.A.[Mostafa A.],
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Chen, D.Q.[Dong-Qing],
Casanova, M.F.[Manuel F.],
Surface Modeling of the Corpus Callosum from MRI Scans,
ISVC10(III: 9-18).
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1011
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Xia, Y.[Yong],
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Feng, D.D.[David Dagan],
Dual-modality 3D brain PET-CT image segmentation based on probabilistic
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ICIP10(2557-2560).
IEEE DOI
1009
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Chen, W.[Wenan],
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Actual Midline Estimation from Brain CT Scan Using Multiple Regions
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ICPR10(2552-2555).
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1008
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Atlas-based segmentation of brain MR images using least square support
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IPTA10(306-310).
IEEE DOI
1007
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Saha, S.[Somojit],
Das, S.K.[Sarit Kumar],
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A new segmentation technique for brain and head from high resolution MR
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IPTA10(288-293).
IEEE DOI
1007
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Kartaszynski, R.H.[Rafal Henryk],
Mikolajczak, P.[Pawel],
MRI Brain Segmentation Using Cellular Automaton Approach,
ICCVG10(II: 17-24).
Springer DOI
1009
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Chen, W.[Wenan],
Smith, R.[Rebecca],
Nabizadeh, N.[Nooshin],
Ward, K.[Kevin],
Cockrell, C.[Charles],
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Najarian, K.[Kayvan],
Texture Analysis of Brain CT Scans for ICP Prediction,
ICISP10(568-575).
Springer DOI
1006
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Dutailly, B.[Bruno],
Coqueugniot, H.[Helene],
Desbarats, P.[Pascal],
Gueorguieva, S.[Stefka],
Synave, R.[Remi],
3D surface reconstruction using HMH algorithm,
ICIP09(2505-2508).
IEEE DOI
0911
BibRef
Gong, T.X.[Tian-Xia],
Li, S.M.[Shi-Miao],
Tan, C.L.[Chew Lim],
Pang, B.C.[Boon Chuan],
Lim, C.C.T.[C.C. Tchoyoson],
Lee, C.K.[Cheng Kiang],
Tian, Q.[Qi],
Zhang, Z.[Zhuo],
Automatic Pathology Annotation on Medical Images:
A Statistical Machine Translation Framework,
ICPR10(2504-2507).
IEEE DOI
1008
BibRef
Gong, T.X.[Tian-Xia],
Li, S.M.[Shi-Miao],
Wang, J.[Jie],
Tan, C.L.[Chew Lim],
Pang, B.C.[Boon Chuan],
Lim, C.C.T.[C. C. Tchoyoson],
Lee, C.K.[Cheng Kiang],
Tian, Q.[Qi],
Zhang, Z.[Zhuo],
Automatic labeling and classification of brain CT images,
ICIP11(1581-1584).
IEEE DOI
1201
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Liu, R.Z.[Rui-Zhe],
Li, S.M.[Shi-Miao],
Tan, C.L.[Chew Lim],
Pang, B.C.[Boon Chuan],
Lim, C.C.T.[C. C. Tchoyoson],
Lee, C.K.[Cheng Kiang],
Tian, Q.[Qi],
Zhang, Z.[Zhuo],
Fast traumatic brain injury CT slice indexing via anatomical feature
classification,
ICIP10(4377-4380).
IEEE DOI
1009
BibRef
Earlier:
From hemorrhage to midline shift: A new method of tracing the deformed
midline in traumatic brain injury CT images,
ICIP09(2637-2640).
IEEE DOI
0911
BibRef
Liu, R.Z.[Rui-Zhe],
Tan, C.L.[Chew Lim],
Leong, T.Y.[Tze Yun],
Lee, C.K.[Cheng Kiang],
Pang, B.C.[Boon Chuan],
Lim, C.C.T.[C. C. Tchoyoson],
Tian, Q.[Qi],
Tang, S.S.[Sui-Sheng],
Zhang, Z.[Zhuo],
Hemorrhage slices detection in brain CT images,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Waddell, C.,
Pratt, J.A.,
Porr, B.,
Ewing, S.,
Deep Brain Stimulation Artifact Removal Through Under-Sampling and
Cubic-Spline Interpolation,
CISP09(1-5).
IEEE DOI
0910
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Sun, Y.[Yu],
Bhanu, B.[Bir],
3D Filtering for Injury Detection in Brain MRI,
ICPR10(2568-2571).
IEEE DOI
1008
BibRef
Sun, Y.[Yu],
Bhanu, B.[Bir],
Bhanu, S.[Shiv],
Symmetry-integrated injury detection for brain MRI,
ICIP09(661-664).
IEEE DOI
0911
BibRef
And:
Automatic symmetry-integrated brain injury detection in MRI sequences,
MMBIA09(79-86).
IEEE DOI
0906
BibRef
Sun, Y.[Yu],
Bhanu, B.[Bir],
Symmetry integrated region-based image segmentation,
CVPR09(826-831).
IEEE DOI
0906
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Shi, F.[Feng],
Yap, P.T.[Pew-Thian],
Fan, Y.[Yong],
Cheng, J.Z.[Jie-Zhi],
Wald, L.L.[Lawrence L.],
Gerig, G.[Guido],
Lin, W.L.[Wei-Li],
Shen, D.G.[Ding-Gang],
Cortical enhanced tissue segmentation of neonatal brain MR images
acquired by a dedicated phased array coil,
MMBIA09(39-45).
IEEE DOI
0906
BibRef
Flores-Tapia, D.[Daniel],
Thomas, G.[Gabriel],
McCurdy, B.[Boyd],
Pistorius, S.[Stephen],
Brain MRI Segmentation Based on the Rényi's Fractal Dimension,
ICIAR09(737-748).
Springer DOI
0907
BibRef
El-Baz, A.,
Casanova, M.,
Gimel'farb, G.L.,
Mott, M.,
Switala, A.,
Vanbogaert, E.,
McCracken, R.,
A New CAD System for Early Diagnosis of
Dyslexic Brains,
ICIP08(1820-1823).
IEEE DOI
0810
See also Automatic analysis of 3D low dose CT images for early diagnosis of lung cancer.
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Rousseau, F.[François],
Brain Hallucination,
ECCV08(I: 497-508).
Springer DOI
0810
Generate high resolution brain image from low resolution images.
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Chung, M.K.[Moo K.],
Kelley, D.J.[Daniel J.],
Dalton, K.M.[Kim M.],
Davidon, R.J.[Richard J.],
Quantifying cortical surface asymmetry via logistic discriminant
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
White Matter Fiber Tractography MRI .