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Earlier:
A fractal dimension based optimal wavelet packet analysis technique for
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Coloured texture analysis; Feature extraction; Histopathological
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Earlier:
An Efficient Framework for Brain Tumor Segmentation in Magnetic
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Bourouis, S.[Sami],
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SVM; Segmentation; Feature selection; Fusion; Follow-up system; Brain
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Feature extraction
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support vector machine, radial basis function, binarized image
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tumor
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Yuan-Hsiung, T.[Tsai],
Ho-Ling, L.[Liu],
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1202
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Büchler, P.[Philippe],
Reyes, M.[Mauricio],
Integrated segmentation of brain tumor images for radiotherapy and
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IJIST(23), No. 1, March 2013, pp. 59-63.
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1303
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1307
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Discriminant Convex Non-negative Matrix Factorization
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DOI Link
1405
bilateral filter
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Kwon, D.J.[Dong-Jin],
Niethammer, M.,
Akbari, H.,
Bilello, M.,
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biomedical MRI
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A complete automated algorithm for segmentation of tissues and
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IJIST(24), No. 4, 2014, pp. 313-325.
DOI Link
1411
image segmentation
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Balasubramani, P.[Perumal],
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Efficient image compression techniques for compressing multimodal
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1506
MRI images
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1411
Zernike polynomial
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1502
brain tumor, clustering, segmentation, thresholding, Fuzzy c-means
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1509
semi-automatic segmentation
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Slotboom, J.,
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Ayache, N.,
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Collins, D.L.,
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Das, T.,
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Demiralp, C.,
Durst, C.R.,
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Geremia, E.,
Glocker, B.,
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Guo, X.T.[Xiao-Tao],
Hamamci, A.,
Iftekharuddin, K.M.,
Jena, R.,
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Konukoglu, E.,
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Meier, R.,
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Reza, S.M.S.,
Ryan, M.,
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Shotton, J.,
Silva, C.A.,
Sousa, N.,
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Szekely, G.,
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Thomas, O.M.,
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benchmark testing
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enhancement
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biomedical MRI
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Biomedical image processing
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IEEE DOI
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Stefano, A.[Alessandro],
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Ippolito, M.[Massimo],
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DOI Link
1604
random walk
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Pereira, S.,
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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI
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Brain modeling
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Thirumurugan, P.,
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contourlet transform
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Kathirvel, R.,
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1708
accuracy, brain tumor, classifier, contourlet transform, , features
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Kathirvel, R.,
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1708
brain tumors, detection, diagnosis, features, , tissues
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Thirumurugan, P.,
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DOI Link
1606
gray matter, white matter, CSF, brain tumor, brain tissue
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Kumarganesh, S.,
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1701
brain tumor
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Thiruvenkadam, K.[Kalaiselvi],
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Fully automatic method for segmentation of brain tumor from
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DOI Link
1701
clustering, fuzzy c-means, segmentation, tumor, wavelet
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Vishnuvarthanan, G.,
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Vishnuvarthanan, N.A.[N. Anitha],
Prasath, T.A.[T. Arun],
Kannan, M.,
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1704
MPSO-based FCM
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Angulakshmi, M.,
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1704
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Sivakumar, P.,
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CANFIS based glioma brain tumor classification and retrieval system
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DOI Link
1706
brain tumor, CANFIS, classification, retrieval, segmentation
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Farhi, L.[Lubna],
Yusuf, A.[Adeel],
Raza, R.H.[Rana Hammad],
Adaptive stochastic segmentation via energy-convergence for brain
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JVCIR(46), No. 1, 2017, pp. 303-311.
Elsevier DOI
1706
Active, contours
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Kaur, T.[Taranjit],
Saini, B.S.[Barjinder Singh],
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Quantitative metric for MR brain tumour grade classification using
sample space density measure of analytic intrinsic mode function
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IET-IPR(11), No. 8, August 2017, pp. 620-632.
DOI Link
1708
BibRef
Ramakrishnan, T.,
Sankaragomathi, B.,
A professional estimate on the computed tomography brain tumor images
using SVM-SMO for classification and MRG-GWO for segmentation,
PRL(94), No. 1, 2017, pp. 163-171.
Elsevier DOI
1708
Feature, extraction
BibRef
Rajinikanth, V.,
Satapathy, S.C.[Suresh Chandra],
Fernandes, S.L.[Steven Lawrence],
Nachiappan, S.,
Entropy based segmentation of tumor from brain MR images:
A study with teaching learning based optimization,
PRL(94), No. 1, 2017, pp. 87-95.
Elsevier DOI
1708
BibRef
Arunachalam, M.[Murugan],
Savarimuthu, S.R.[Sabeenian Royappan],
An efficient and automatic glioblastoma brain tumor detection using
shift-invariant shearlet transform and neural networks,
IJIST(27), No. 3, 2017, pp. 216-226.
DOI Link
1708
brain image, NSCT multiresolution, SIST enhancement,
texture features, , classification
BibRef
Rufus, N.H.A.[N. Herald Anantha],
Selvathi, D.,
Performance analysis of computer aided brain tumor detection system
using ANFIS classifier,
IJIST(27), No. 3, 2017, pp. 273-280.
DOI Link
1708
brain image, classifier, features, GLCM, , tumor
BibRef
Gupta, M.[Manu],
Rajagopalan, V.[Venkateswaran],
Pioro, E.P.[Erik P.],
Prabhakar Rao, B.V.V.S.N.,
Volumetric analysis of MR images for glioma classification and their
effect on brain tissues,
SIViP(11), No. 7, October 2017, pp. 1337-1345.
Springer DOI
1708
BibRef
Ravì, D.,
Fabelo, H.,
Callic, G.M.,
Yang, G.Z.,
Manifold Embedding and Semantic Segmentation for Intraoperative
Guidance With Hyperspectral Brain Imaging,
MedImg(36), No. 9, September 2017, pp. 1845-1857.
IEEE DOI
1709
biological tissues, brain, cancer, hyperspectral imaging,
image classification, image segmentation,
medical image processing, semantic networks,
stochastic processes,
T-distributed stochastic neighbor approach, brain surgery,
dimensionality reduction scheme, hyperspectral brain imaging,
semantic segmentation technique, semantic texton forest,
tissue classification, tumor classification map, Brain, Cancer,
Hyperspectral imaging, Image segmentation, Manifolds, Semantics,
Tumors, Manifold embedding, brain cancer detection,
hyperspectral imaging, semantic, segmentation
BibRef
Anitha, R.,
Raja, D.S.S.[D. Siva Sundhara],
Segmentation of glioma tumors using convolutional neural networks,
IJIST(27), No. 4, 2017, pp. 354-360.
DOI Link
1712
brain tumors, classifier, features, glioma, image fusion
BibRef
Alagarsamy, S.[Saravanan],
Kamatchi, K.[Kartheeban],
Govindaraj, V.[Vishnuvarthanan],
Thiyagarajan, A.P.[Arun Prasath],
A fully automated hybrid methodology using Cuckoo-based fuzzy
clustering technique for magnetic resonance brain image segmentation,
IJIST(27), No. 4, 2017, pp. 317-332.
DOI Link
1712
Cuckoo-Based Search, interval type-2 fuzzy clustering,
MR brain image segmentation, tumor identification
BibRef
Rundo, L.[Leonardo],
Militello, C.[Carmelo],
Tangherloni, A.[Andrea],
Russo, G.[Giorgio],
Vitabile, S.[Salvatore],
Gilardi, M.C.[Maria Carla],
Mauri, G.[Giancarlo],
NeXt for neuro-radiosurgery: A fully automatic approach for necrosis
extraction in brain tumor MRI using an unsupervised machine learning
technique,
IJIST(28), No. 1, 2018, pp. 21-37.
DOI Link
1802
brain tumors, magnetic resonance imaging, necrosis extraction,
neuro-radiosurgery treatments, unsupervised Fuzzy C-Means clustering
BibRef
Ding, Y.[Yi],
Dong, R.F.[Rong-Feng],
Lan, T.[Tian],
Li, X.R.[Xue-Rui],
Shen, G.Y.[Guang-Yu],
Chen, H.[Hao],
Qin, Z.G.[Zhi-Guang],
Multi-modal brain tumor image segmentation based on SDAE,
IJIST(28), No. 1, 2018, pp. 38-47.
DOI Link
1802
brain tumor segmentation, BRATS 2015, stacked de-noising auto-encoder
BibRef
Anitha, R.,
Raja, D.S.S.[D. Siva Sundhara],
Development of computer-aided approach for brain tumor detection
using random forest classifier,
IJIST(28), No. 1, 2018, pp. 48-53.
DOI Link
1802
abnormal patterns, brain tumors, classification, diagnose, segmentation
BibRef
Sasikanth, S.,
Kumar, S.S.[S. Suresh],
Glioma tumor detection in brain MRI image using ANFIS-based
normalized graph cut approach,
IJIST(28), No. 1, 2018, pp. 64-71.
DOI Link
1802
classifier, glioma tumor, graph cut approach,
orientation analysis, validation
BibRef
Wu, G.Q.[Guo-Qing],
Chen, Y.S.[Yin-Sheng],
Wang, Y.Y.[Yuan-Yuan],
Yu, J.H.[Jin-Hua],
Lv, X.F.[Xiao-Fei],
Ju, X.[Xue],
Shi, Z.F.[Zhi-Feng],
Chen, L.[Liang],
Chen, Z.P.[Zhong-Ping],
Sparse Representation-Based Radiomics for the Diagnosis of Brain
Tumors,
MedImg(37), No. 4, April 2018, pp. 893-905.
IEEE DOI
1804
Cancer, Dictionaries, Estimation, Feature extraction,
Medical diagnostic imaging, Tumors, Brain tumors,
tumor differentiation
BibRef
Anantha, N.H.[N. Herald],
Selvathi, R.D.[Rufus D.],
Performance analysis of brain tissues and tumor detection and grading
system using ANFIS classifier,
IJIST(28), No. 2, 2018, pp. 77-85.
WWW Link.
1806
BibRef
Pinto, A.[Adriano],
Pereira, S.[Sérgio],
Rasteiro, D.[Deolinda],
Silva, C.A.[Carlos A.],
Hierarchical brain tumour segmentation using extremely randomized
trees,
PR(82), 2018, pp. 105-117.
Elsevier DOI
1806
Brain tumour, Magnetic resonance imaging, Image segmentation,
Hierarchy of classifiers, Extremely randomized trees, Machine learning
BibRef
Izadyyazdanabadi, M.[Mohammadhassan],
Belykh, E.[Evgenii],
Mooney, M.[Michael],
Martirosyan, N.[Nikolay],
Eschbacher, J.[Jennifer],
Nakaji, P.[Peter],
Preul, M.C.[Mark C.],
Yang, Y.Z.[Ye-Zhou],
Convolutional neural networks: Ensemble modeling, fine-tuning and
unsupervised semantic localization for neurosurgical CLE images,
JVCIR(54), 2018, pp. 10-20.
Elsevier DOI
1806
Neural network, Unsupervised localization, Ensemble modeling,
Brain tumor, Confocal laser endomicroscopy, Surgical vision
BibRef
Arnaud, A.,
Forbes, F.,
Coquery, N.,
Collomb, N.,
Lemasson, B.,
Barbier, E.L.,
Fully Automatic Lesion Localization and Characterization: Application
to Brain Tumors Using Multiparametric Quantitative MRI Data,
MedImg(37), No. 7, July 2018, pp. 1678-1689.
IEEE DOI
1808
biomedical MRI, brain, cancer, feature extraction,
Gaussian distribution, image segmentation,
fingerprint model
BibRef
Ma, C.,
Luo, G.,
Wang, K.,
Concatenated and Connected Random Forests With Multiscale Patch
Driven Active Contour Model for Automated Brain Tumor Segmentation of
MR Images,
MedImg(37), No. 8, August 2018, pp. 1943-1954.
IEEE DOI
1808
Image segmentation, Tumors, Brain modeling,
Magnetic resonance imaging, Active contours, Radio frequency,
multiscale patch
BibRef
Tang, Z.,
Ahmad, S.,
Yap, P.,
Shen, D.,
Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank
Based Image Recovery,
MedImg(37), No. 10, October 2018, pp. 2224-2235.
IEEE DOI
1810
Brain, Tumors, Image segmentation, Pathology,
Convergence, Radiology, Low-rank,
multi-atlas segmentation
BibRef
Kermi, A.[Adel],
Andjouh, K.[Khaled],
Zidane, F.[Ferhat],
Fully automated brain tumour segmentation system in 3D-MRI using
symmetry analysis of brain and level sets,
IET-IPR(12), No. 11, November 2018, pp. 1964-1971.
DOI Link
1810
BibRef
Angulakshmi, M.,
Lakshmi Priya, G.G.,
Walsh Hadamard kernel-based texture feature for multimodal MRI brain
tumour segmentation,
IJIST(28), No. 4, December 2018, pp. 254-266.
WWW Link.
1811
BibRef
Selvapandian, A.,
Manivannan, K.,
Performance analysis of meningioma brain tumor classifications based on
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IJIST(28), No. 4, December 2018, pp. 295-301.
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1811
BibRef
Lian, C.,
Ruan, S.,
Denœux, T.,
Li, H.,
Vera, P.,
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and
Fusion Based on Belief Functions,
IP(28), No. 2, February 2019, pp. 755-766.
IEEE DOI
1811
cancer, computerised tomography, image fusion, image segmentation,
iterative methods, lung, medical image processing,
PET-CT
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Zhou, T.,
Canu, S.,
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Latent Correlation Representation Learning for Brain Tumor
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IP(30), 2021, pp. 4263-4274.
IEEE DOI
2104
Tumors, Image segmentation, Correlation,
Magnetic resonance imaging, Brain modeling, Feature extraction,
deep learning
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Chen, S.C.[Sheng-Cong],
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PR(88), 2019, pp. 90-100.
Elsevier DOI
1901
Brain tumor segmentation, Dual-force network,
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JVCIR(58), 2019, pp. 316-322.
Elsevier DOI
1901
MR image segmentation, Convolutional Neural Network, Fully CRF
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Saman, S.[Sangeetha],
Narayanan, S.J.[Swathi Jamjala],
Survey on brain tumor segmentation and feature extraction of MR images,
MultInfoRetr(8), No. 2, June 2019, pp. 79-99.
Springer DOI
1906
Survey, Brain Tumors.
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Rezaei, K.[Kimia],
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Mahmoodzadeh, A.[Azar],
Multi-objective differential evolution-based ensemble method for brain
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IET-IPR(13), No. 9, 18 July 2019, pp. 1421-1430.
DOI Link
1907
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Rajagopal, R.,
Glioma brain tumor detection and segmentation using weighting random
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IJIST(29), No. 3, September 2019, pp. 353-359.
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1908
BibRef
Kale, V.V.[Vandana V.],
Hamde, S.T.[Satish T.],
Holambe, R.S.[Raghunath S.],
Brain disease diagnosis using local binary pattern and steerable
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Springer DOI
1908
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Gtifa, W.[Wafa],
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3D brain tumor segmentation in MRI images based on a modified PSO
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1911
2D images, 3D brain tumor segmentation, modified particle swarm optimization
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Nagarathinam, E.[Ezhilmathi],
Ponnuchamy, T.[Thirumurugan],
Image registration-based brain tumor detection and segmentation using
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1911
abnormal cells, classifications, detection, segmentation, tumor
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Johnpeter, J.H.[Jasmine Hephzipah],
Ponnuchamy, T.[Thirumurugan],
Computer aided automated detection and classification of brain tumors
using CANFIS classification method,
IJIST(29), No. 4, 2019, pp. 431-438.
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1911
abnormal, brain, classification, statistical features, tumors
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Gilanie, G.[Ghulam],
Bajwa, U.I.[Usama Ijaz],
Waraich, M.M.[Mustansar Mahmood],
Habib, Z.[Zulfiqar],
Automated and reliable brain radiology with texture analysis of
magnetic resonance imaging and cross datasets validation,
IJIST(29), No. 4, 2019, pp. 531-538.
DOI Link
1911
brain tumor diagnosis, cross dataset validation,
MRI texture analysis, neoplastic and non-neoplastic tissues,
primary and secondary brain tumor
BibRef
Tamilmani, G.,
Sivakumari, S.,
Early detection of brain cancer using association allotment
hierarchical clustering,
IJIST(29), No. 4, 2019, pp. 617-632.
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1911
association allotment hierarchical clustering,
gray wolf optimization,
mutual piece-wise linear transformation filtering
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Meng, H.,
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Tian, J.,
Adaptive Gaussian Weighted Laplace Prior Regularization Enables
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MedImg(38), No. 12, December 2019, pp. 2726-2734.
IEEE DOI
1912
Fluorescence, Image reconstruction, Imaging, In vivo, Kernel, Probes,
Tumors, Fluorescence tomography, multi-modality fusion, brain
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Sharif, M.[Muhammad],
Amin, J.[Javaria],
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BSE, PSO, GA, LBP, Deep features, ANN
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Amin, J.[Javaria],
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Elsevier DOI
2001
Sequences, CNN, DWT, Global thresholding, Filter
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Sharif, M.I.[Muhammad Irfan],
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Elsevier DOI
2001
Brain tumor, Contrast improvement, Deep saliency method,
Features extraction, Optimization, Recognition
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Mahesh, K.M.[K. Michael],
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Multiclassifier for severity-level categorization of glioma tumors
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DOI Link
2002
deep convolutional neural networks,
Jaya optimization algorithm, multimodal MRI brain images,
severity-level classification
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Mammoli, D.,
Gordon, J.,
Autry, A.,
Larson, P.E.Z.,
Li, Y.,
Chen, H.,
Chung, B.,
Shin, P.,
van Criekinge, M.,
Carvajal, L.,
Slater, J.B.,
Bok, R.,
Crane, J.,
Xu, D.,
Chang, S.,
Vigneron, D.B.,
Kinetic Modeling of Hyperpolarized Carbon-13 Pyruvate Metabolism in
the Human Brain,
MedImg(39), No. 2, February 2020, pp. 320-327.
IEEE DOI
2002
Brain cancer, dissolution dynamic nuclear polarization,
hyperpolarized MRI, kinetic modeling, kPL, kPB, metabolic imaging
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Hachemi, B.[Belkacem],
Chama, Z.[Zouaoui],
Alim-Ferhat, F.[Fatiha],
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Abderrahmane, A.[Abdelkader],
Anani, M.[Macho],
Choquet, C.[Catherine],
Fully automatic multisegmentation approach for magnetic resonance
imaging brain tumor detection using improved region-growing and
quasi-Monte Carlo-expectation maximization algorithm,
IJIST(30), No. 1, 2020, pp. 104-111.
DOI Link
2002
brain tumor, expectation maximization, multisegmentation,
quasi-Monte Carlo, region growing
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Peng, S.T.[Su-Ting],
Chen, W.[Wei],
Sun, J.W.[Jia-Wei],
Liu, B.Q.[Bo-Qiang],
Multi-Scale 3D U-Nets:
An approach to automatic segmentation of brain tumor,
IJIST(30), No. 1, 2020, pp. 5-17.
DOI Link
2002
brain tumor, CNN, deep learning, gliomas, image segmentation
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Zhou, C.,
Ding, C.,
Wang, X.,
Lu, Z.,
Tao, D.,
One-Pass Multi-Task Networks With Cross-Task Guided Attention for
Brain Tumor Segmentation,
IP(29), 2020, pp. 4516-4529.
IEEE DOI
2003
Tumors, Task analysis, Image segmentation, Brain modeling,
Computational modeling, Training, Magnetic resonance imaging,
channel attention
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Nasor, M.[Mohamed],
Obaid, W.[Walid],
Detection and localisation of multiple brain tumours by object counting
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IET-IPR(14), No. 4, 27 March 2020, pp. 615-620.
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2003
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CapsNet topology to classify tumours from brain images and comparative
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IET-IPR(14), No. 5, 17 April 2020, pp. 882-889.
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2004
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Tiwari, A.[Arti],
Srivastava, S.[Shilpa],
Pant, M.[Millie],
Brain tumor segmentation and classification from magnetic resonance
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Elsevier DOI
2004
Brain tumor segmentation, Glioma, Neoplasia, Magnetic resonance imaging (MRI)
BibRef
Zhang, W.X.[Wen-Xue],
Jian, J.B.[Jian-Bo],
Sun, C.[Cuiyun],
Chen, J.[Jie],
Lv, W.J.[Wen-Juan],
Sun, M.Y.[Meng-Yu],
Zhao, Y.Q.[Yu-Qing],
Zhao, Q.[Qi],
Hu, C.H.[Chun-Hong],
High-resolution 3D imaging of microvascular architecture in human
glioma tissues using X-ray phase-contrast computed tomography as a
potential adjunct to histopathology,
IJIST(30), No. 2, 2020, pp. 464-472.
DOI Link
2005
glioma, microthrombi, microvascular architecture,
X-ray phase-contrast computed tomography
BibRef
Lu, Z.Y.[Zhen-Yu],
Bai, Y.Z.[Yan-Zhong],
Chen, Y.[Yi],
Su, C.Q.[Chun-Qiu],
Lu, S.S.[Shan-Shan],
Zhan, T.M.[Tian-Ming],
Hong, X.N.[Xun-Ning],
Wang, S.H.[Shui-Hua],
The classification of gliomas based on a Pyramid dilated convolution
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Elsevier DOI
2005
Gliomas, Classification, Deep learning, ResNet, Dilated convolution
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Dandil, E.[Emre],
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Automatic grading of brain tumours using LSTM neural networks on
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IET-IPR(14), No. 10, August 2020, pp. 1967-1979.
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2008
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Chithra, P.L.,
Dheepa, G.,
Di-phase midway convolution and deconvolution network for brain tumor
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IJIST(30), No. 3, 2020, pp. 674-686.
DOI Link
2008
brain tumor segmentation,
di-phase midway convolution and deconvolution network,
upsampling
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Zhang, D.,
Huang, G.,
Zhang, Q.,
Han, J.,
Han, J.,
Wang, Y.,
Yu, Y.,
Exploring Task Structure for Brain Tumor Segmentation From
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IP(29), 2020, pp. 9032-9043.
IEEE DOI
2009
Tumors, Task analysis, Image segmentation,
Brain modeling,
supervised learning
BibRef
Kurmi, Y.[Yashwant],
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Classification of magnetic resonance images for brain tumour detection,
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2010
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Method of multi-region tumour segmentation in brain MRI images using
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2010
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Liu, X.M.[Xiao-Ming],
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Transparency-guided ensemble convolutional neural network for the
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Elsevier DOI
2010
Pseudo progression, Glioblastoma multiforme,
Diffusion tensor imaging (DTI), Ensemble CNN
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Amin, J.[Javeria],
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Elsevier DOI
2011
Cells, Tumors, Segmentation, Lesion, Tissues
BibRef
Zhang, D.W.[Ding-Wen],
Huang, G.H.[Guo-Hai],
Zhang, Q.A.[Qi-Ang],
Han, J.G.[Jun-Gong],
Han, J.W.[Jun-Wei],
Yu, Y.Z.[Yi-Zhou],
Cross-modality deep feature learning for brain tumor segmentation,
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Elsevier DOI
2011
Brain tumor segmentation, Cross-modality feature transition,
Cross-modality feature fusion, Feature learning
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Yepuganti, K.[Karuna],
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Narasimhulu, C.V.,
Segmentation of tumor using PCA based modified fuzzy C means
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2011
brain tumor, DWT, feature extraction, fuzzy C means and MRI
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Leena, B.[Bojaraj],
Jayanthi, A.[Annamalai],
Brain tumor segmentation and classification via adaptive CLFAHE with
hybrid classification,
IJIST(30), No. 4, 2020, pp. 874-898.
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2011
brain tumor classification, feature extraction, optimization,
segmentation, skull stripping
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Kalaiselvi, T.[Thiruvenkadam],
Padmapriya, T.[Thiyagarajan],
Sriramakrishnan, P.[Padmanaban],
Priyadharshini, V.[Venugopal],
Development of automatic glioma brain tumor detection system using
deep convolutional neural networks,
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2011
BraTS, deep learning, glioma tumor, neural networks,
tumor detection, WBA
BibRef
Afshar, P.,
Mohammadi, A.,
Plataniotis, K.N.,
BayesCap: A Bayesian Approach to Brain Tumor Classification Using
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IEEE DOI
2012
BibRef
Earlier: A1, A3, A2:
Capsule Networks' Interpretability for Brain Tumor Classification Via
Radiomics Analyses,
ICIP19(3816-3820)
IEEE DOI
1910
BibRef
And: A1, A2, A3:
Brain Tumor Type Classification via Capsule Networks,
ICIP18(3129-3133)
IEEE DOI
1809
Bayes methods, Uncertainty, Tumors, Predictive models, Deep learning,
Measurement uncertainty, Brain modeling,
Radiomics.
Brain Tumor Classification, Capsule Networks, Explainability.
Feature extraction, Magnetic resonance imaging, Neurons,
Computer architecture, Cancer, Training data,
Convolutional neural networks
BibRef
Afshar, P.,
Shahroudnejad, A.,
Mohammadi, A.,
Plataniotis, K.N.,
CARISI: Convolutional Autoencoder-Based Inter-Slice Interpolation of
Brain Tumor Volumetric Images,
ICIP18(1458-1462)
IEEE DOI
1809
Tumors, Interpolation,
Image reconstruction, Shape, Computed tomography, Convolution,
Convolutional auto-encoder
BibRef
Sran, P.K.[Paramveer Kaur],
Gupta, S.[Savita],
Singh, S.[Sukhwinder],
Integrating saliency with fuzzy thresholding for brain tumor
extraction in MR images,
JVCIR(74), 2021, pp. 102964.
Elsevier DOI
2101
Saliency, Fuzzy, Segmentation, ROI, Medical Images
BibRef
Ragupathy, B.[Balakumaresan],
Karunakaran, M.[Manivannan],
A deep learning model integrating convolution neural network and
multiple kernel K means clustering for segmenting brain tumor in
magnetic resonance images,
IJIST(31), No. 1, 2021, pp. 118-127.
DOI Link
2102
convolutional neural network, magnetic resonance image,
multi kernel K means clustering, segmentation, tumor detection
BibRef
Thiruvenkadam, K.[Kalaiselvi],
Nagarajan, K.[Kalaichelvi],
Fully automatic brain tumor extraction and tissue segmentation from
multimodal MRI brain images,
IJIST(31), No. 1, 2021, pp. 336-350.
DOI Link
2102
3D volume estimation, bit plane slicing, brain tumor,
extended maxima, FCM, inverse log transform, MRI
BibRef
Hu, J.Y.[Jing-Yu],
Gu, X.J.[Xiao-Jing],
Gu, X.S.[Xing-Sheng],
Dual-pathway DenseNets with fully lateral connections for multimodal
brain tumor segmentation,
IJIST(31), No. 1, 2021, pp. 364-378.
DOI Link
2102
brain tumor, convolutional network, dense network,
multimodalities, segmentation
BibRef
Ragupathy, B.[Balakumaresan],
Karunakaran, M.[Manivannan],
A fuzzy logic-based meningioma tumor detection in magnetic resonance
brain images using CANFIS and U-Net CNN classification,
IJIST(31), No. 1, 2021, pp. 379-390.
DOI Link
2102
brain, classifications, features, meningioma, tumor
BibRef
Mohamed, L.A.[Linda Ait],
Cherfa, A.[Assia],
Cherfa, Y.[Yazid],
Belkhamsa, N.[Noureddine],
Alim-Ferhat, F.[Fatiha],
Hybrid method combining superpixel, supervised learning, and random
walk for glioma segmentation,
IJIST(31), No. 1, 2021, pp. 288-301.
DOI Link
2102
glioma tumor, random forest, random walk, segmentation, superpixels
BibRef
Singh, R.[Rahul],
Goel, A.[Aditya],
Raghuvanshi, D.K.,
Ensemble-based glioma grade classification using Gabor filter bank and
rotation forest,
IET-IPR(14), No. 15, 15 December 2020, pp. 3851-3858.
DOI Link
2103
BibRef
Shirali, A.[Armaghan],
Shiri, N.[Nabiollah],
Diagnosis of brain tumours by MRI binarisation with variable fuzzy
level,
IET-IPR(14), No. 16, 19 December 2020, pp. 4269-4276.
DOI Link
2103
BibRef
Kamli, A.[Adel],
Saouli, R.[Rachida],
Batatia, H.[Hadj],
Ben Naceur, M.[Mostefa],
Youkana, I.[Imane],
Synthetic medical image generator for data augmentation and
anonymisation based on generative adversarial network for glioblastoma
tumors growth prediction,
IET-IPR(14), No. 16, 19 December 2020, pp. 4248-4257.
DOI Link
2103
BibRef
Yu, B.T.[Bi-Ting],
Zhou, L.P.[Lu-Ping],
Wang, L.[Lei],
Yang, W.Q.[Wan-Qi],
Yang, M.[Ming],
Bourgeat, P.[Pierrick],
Fripp, J.[Jurgen],
SA-LuT-Nets: Learning Sample-Adaptive Intensity Lookup Tables for
Brain Tumor Segmentation,
MedImg(40), No. 5, May 2021, pp. 1417-1427.
IEEE DOI
2105
Image segmentation, Table lookup, Tumors,
Task analysis, Solid modeling,
neural network
BibRef
Jeevanantham, V.,
MohanBabu, G.,
Detection and diagnosis of brain tumors-framework using extreme
machine learning and CANFIS classification algorithms,
IJIST(31), No. 2, 2021, pp. 540-547.
DOI Link
2105
brain, features, machine learning, transforms, tumors
BibRef
Kaushal, B.[Bhakti],
Patil, M.D.[Mukesh D.],
Birajdar, G.K.[Gajanan K.],
Fractional wavelet transform based diagnostic system for brain tumor
detection in MR imaging,
IJIST(31), No. 2, 2021, pp. 575-591.
DOI Link
2105
brain tumor detection, discrete wavelet transform,
fractional Fourier transform, fractional wavelet transform
BibRef
Hu, A.[An],
Razmjooy, N.[Navid],
Brain tumor diagnosis based on metaheuristics and deep learning,
IJIST(31), No. 2, 2021, pp. 657-669.
DOI Link
2105
brain tumor, deep belief network, feature extraction,
feature selection, classification, improved seagull optimization algorithm
BibRef
Dzulkifli, F.A.[Fahmi Akmal],
Mashor, M.Y.[Mohd Yusoff],
Jaafar, H.[Hasnan],
Colour thresholding-based automatic Ki67 counting procedure for
Immunohistochemical staining in meningioma,
IJCVR(11), No. 3, 2021, pp. 279-298.
DOI Link
2106
BibRef
Lerche, C.W.[Christoph W.],
Radomski, T.[Timon],
Lohmann, P.[Philipp],
Caldeira, L.[Liliana],
Brambilla, C.R.[Cláudia Régio],
Tellmann, L.[Lutz],
Scheins, J.[Jürgen],
Kops, E.R.[Elena Rota],
Galldiks, N.[Norbert],
Langen, K.J.[Karl-Josef],
Herzog, H.[Hans],
Shah, N.J.[N. Jon],
A Linearized Fit Model for Robust Shape Parameterization of FET-PET
TACs,
MedImg(40), No. 7, July 2021, pp. 1852-1862.
IEEE DOI
2107
Neuroscience, Kinetic theory, Tumors, Brain modeling, Shape, Imaging,
Field effect transistors, FET PET, glioma classification, parametric imaging
BibRef
Bagyaraj, S.[Sanjeevirayar],
Tamilselvi, R.[Rajendran],
Gani, P.B.M.[Parisa Beham Mohamed],
Sabarinathan, D.[Devanathan],
Brain tumour cell segmentation and detection using deep learning
networks,
IET-IPR(15), No. 10, 2021, pp. 2363-2371.
DOI Link
2108
BibRef
Alaraimi, S.[Saleh],
Okedu, K.E.[Kenneth E.],
Tianfield, H.[Hugo],
Holden, R.[Richard],
Uthmani, O.[Omair],
Transfer learning networks with skip connections for classification
of brain tumors,
IJIST(31), No. 3, 2021, pp. 1564-1582.
DOI Link
2108
AlexNet, convolutional neural network (CNN), deep learning,
GoogLeNet, transfer learning, VGG
BibRef
Deepak, S.,
Ameer, P.M.,
Brain tumour classification using siamese neural network and
neighbourhood analysis in embedded feature space,
IJIST(31), No. 3, 2021, pp. 1655-1669.
DOI Link
2108
brain tumour, classification, Mahalanobis distance,
neighbourhood, Siamese networks
BibRef
Kakarla, J.[Jagadeesh],
Isunuri, B.V.[Bala Venkateswarlu],
Doppalapudi, K.S.[Krishna Sai],
Bylapudi, K.S.R.[Karthik Satya Raghuram],
Three-class classification of brain magnetic resonance images using
average-pooling convolutional neural network,
IJIST(31), No. 3, 2021, pp. 1731-1740.
DOI Link
2108
average pooling, brain tumor classification,
brain tumor dataset, convolutional neural network, three-class classification
BibRef
Rafi, A.[Asra],
Madni, T.M.[Tahir Mustafa],
Janjua, U.I.[Uzair Iqbal],
Ali, M.J.[Muhammad Junaid],
Abid, M.N.[Muhammad Naeem],
Multi-level dilated convolutional neural network for brain tumour
segmentation and multi-view-based radiomics for overall survival
prediction,
IJIST(31), No. 3, 2021, pp. 1519-1535.
DOI Link
2108
brain disease, brain tumour segmentation, dilated convolution,
magnetic resonance imaging, multi-view, overall survival, random forest
BibRef
Mathiyalagan, G.[Gomathi],
Devaraj, D.[Dhanasekaran],
A machine learning classification approach based glioma brain tumor
detection,
IJIST(31), No. 3, 2021, pp. 1424-1436.
DOI Link
2108
features, fuzzy logic, glioma, ridgelet filter, tumor
BibRef
Gurunathan, A.[Akila],
Krishnan, B.[Batri],
Detection and diagnosis of brain tumors using deep learning
convolutional neural networks,
IJIST(31), No. 3, 2021, pp. 1174-1184.
DOI Link
2108
brain, deep learning, machine learning, segmentation, tumors
BibRef
Arumugam, S.[Selvapandian],
Paulraj, S.[Sivakumar],
Selvaraj, N.P.[Nagendra Prabhu],
Brain MR image tumor detection and classification using neuro fuzzy
with binary cuckoo search technique,
IJIST(31), No. 3, 2021, pp. 1185-1196.
DOI Link
2108
binary cuckoo search, magnetic resonance imaging,
neural network, neuro fuzzy with binary cuckoo search,
singular value decomposition
BibRef
Barzegar, Z.[Zeynab],
Jamzad, M.[Mansour],
Fully automated glioma tumour segmentation using anatomical symmetry
plane detection in multimodal brain MRI,
IET-CV(15), No. 7, 2021, pp. 463-473.
DOI Link
2109
BibRef
Anantharajan, S.[Shenbagarajan],
Gunasekaran, S.[Shenbagalakshmi],
Automated brain tumor detection and classification using weighted
fuzzy clustering algorithm, deep auto encoder with barnacle mating
algorithm and random forest classifier techniques,
IJIST(31), No. 4, 2021, pp. 1970-1988.
DOI Link
2112
barnacle mating algorithm, deep auto-encoder,
magnetic resonance imaging, random forest classifier,
weighted fuzzy clustering algorithm
BibRef
Abdelaziz, M.[Mohammed],
Cherfa, Y.[Yazid],
Cherfa, A.[Assia],
Alim-Ferhat, F.[Fatiha],
Automatic brain tumor segmentation for a computer-aided diagnosis
system,
IJIST(31), No. 4, 2021, pp. 2226-2236.
DOI Link
2112
3D reconstruction, Graph Cut, Level Set, Random Forest, segmentation
BibRef
Liu, T.T.[Ting-Ting],
Yuan, Z.[Zhi],
Wu, L.[Li],
Badami, B.[Benjamin],
Optimal brain tumor diagnosis based on deep learning and balanced
sparrow search algorithm,
IJIST(31), No. 4, 2021, pp. 1921-1935.
DOI Link
2112
balanced sparrow search algorithm, brain tumor diagnosis,
discrete wavelet transform, gray-level co-occurrence matrix
BibRef
Zhang, W.[Wenbo],
Yang, G.[Guang],
Huang, H.[He],
Yang, W.J.[Wei-Ji],
Xu, X.M.[Xiao-Mei],
Liu, Y.K.[Yong-Kai],
Lai, X.B.[Xiao-Bo],
ME-Net: Multi-encoder net framework for brain tumor segmentation,
IJIST(31), No. 4, 2021, pp. 1834-1848.
DOI Link
2112
automatic segmentation, brain tumor segmentation,
deep learning, magnetic resonance imaging, multi-encoder net
BibRef
Latif, U.[Urva],
Shahid, A.R.[Ahmad R.],
Raza, B.[Basit],
Ziauddin, S.[Sheikh],
Khan, M.A.[Muazzam A.],
An end-to-end brain tumor segmentation system using
multi-inception-UNET,
IJIST(31), No. 4, 2021, pp. 1803-1816.
DOI Link
2112
brain tumor, BRATS, CNN, inception, UNET
BibRef
Singh, R.[Rahul],
Goel, A.[Aditya],
Raghuvanshi, D.K.[Deepak Kumar],
Binary glioma grading framework employing locality preserving
projections and Gaussian radial basis function support vector machine,
IJIST(31), No. 4, 2021, pp. 2047-2059.
DOI Link
2112
Gabor filter bank,
Gaussian radial basis function-support vector machine (GRBF-SVM),
synthetic minority over-sampling technique (SMOTE)
BibRef
Tunga, P.P.[P. Prakash],
Singh, V.[Vipula],
Aditya, V.S.[V. Sri],
Subramanya, N.,
U-Net Model-Based Classification and Description of Brain Tumor in MRI
Images,
IJIG(21), No. 5 2021, pp. 2140005.
DOI Link
2201
BibRef
Abo Elenein, N.M.[Nagwa M.],
Piao, S.H.[Song-Hao],
Noor, A.[Alam],
Ahmed, P.N.[Pir Noman],
MIRAU-Net: An improved neural network based on U-Net for gliomas
segmentation,
SP:IC(101), 2022, pp. 116553.
Elsevier DOI
2201
Brain tumor segmentation, U-Net, Full convolutional network,
Inception, Residual Module, Attention Gate
BibRef
Siar, M.[Masoumeh],
Teshnehlab, M.[Mohammad],
A combination of feature extraction methods and deep learning for
brain tumour classification,
IET-IPR(16), No. 2, 2022, pp. 416-441.
DOI Link
2201
BibRef
Adu, K.[Kwabena],
Yu, Y.B.[Yong-Bin],
Cai, J.Y.[Jing-Ye],
Asare, I.[Isaac],
Quahin, J.[Jennifer],
The influence of the activation function in a capsule network for
brain tumor type classification,
IJIST(32), No. 1, 2022, pp. 123-143.
DOI Link
2201
activation function, brain tumor classification,
capsule network, convolutional neural network, deep learning
BibRef
Asthana, P.[Pallavi],
Hanmandlu, M.[Madasu],
Vashisth, S.[Sharda],
Classification of brain tumor from magnetic resonance images using
probabilistic features and possibilistic Hanman-Shannon transform
classifier,
IJIST(32), No. 1, 2022, pp. 280-294.
DOI Link
2201
brain tumor, classification, feature extraction,
Hanman-Shannon transform classifier,
probabilistic features
BibRef
Anjum, S.[Sadia],
Hussain, L.[Lal],
Ali, M.[Mushtaq],
Alkinani, M.H.[Monagi H.],
Aziz, W.[Wajid],
Gheller, S.[Sabrina],
Abbasi, A.A.[Adeel Ahmed],
Marchal, A.R.[Ali Raza],
Suresh, H.[Harshini],
Duong, T.Q.[Tim Q.],
Detecting brain tumors using deep learning convolutional neural
network with transfer learning approach,
IJIST(32), No. 1, 2022, pp. 307-323.
DOI Link
2201
brain tumor, convolution neural network, decision tree, deep learning
BibRef
Jiang, L.Q.[Lin-Qi],
Ning, C.Y.[Chun-Yu],
Li, J.Y.[Jing-Yang],
Glioma classification framework based on SE-ResNeXt network and its
optimization,
IET-IPR(16), No. 2, 2022, pp. 596-605.
DOI Link
2201
BibRef
Fang, L.L.[Ling-Ling],
Wang, X.[Xin],
Brain tumor segmentation based on the dual-path network of
multi-modal MRI images,
PR(124), 2022, pp. 108434.
Elsevier DOI
2203
Brain tumor segmentation, Deep learning, Dual-path model,
Magnetic resonance imaging, Multi-modal images
BibRef
Raghavendra, U.,
Gudigar, A.[Anjan],
Rao, T.N.[Tejaswi N.],
Rajinikanth, V.,
Ciaccio, E.J.[Edward J.],
Yeong, C.H.[Chai Hong],
Satapathy, S.C.[Suresh Chandra],
Molinari, F.[Filippo],
Acharya, U.R.[U. Rajendra],
Feature-versus deep learning-based approaches for the automated
detection of brain tumor with magnetic resonance images:
A comparative study,
IJIST(32), No. 2, 2022, pp. 501-516.
DOI Link
2203
brain tumor, classification, deep learning,
elongated quinary patterns, glioblastoma, texture features
BibRef
Shanmugam, S.[Sasikanth],
Surampudi, S.R.[Srinivasa Rao],
A method for detecting and classifying the tumor regions in brain MRI
images using vector index filtering and ANFIS classification process,
IJIST(32), No. 2, 2022, pp. 687-696.
DOI Link
2203
ANFIS, BRATS, fast wavelet transform,
magnetic resonance imaging, vector index filtering
BibRef
Fang, Y.[Ying],
Huang, H.[He],
Yang, W.J.[Wei-Ji],
Xu, X.[Xiaomei],
Jiang, W.W.[Wei-Wei],
Lai, X.B.[Xiao-Bo],
Nonlocal convolutional block attention module VNet for gliomas
automatic segmentation,
IJIST(32), No. 2, 2022, pp. 528-543.
DOI Link
2203
automatic segmentation, convolutional block attention module,
deep learning, glioma, nonlocal block
BibRef
Sun, M.L.[Meng-Li],
Zou, W.[Wei],
Hu, N.[Nan],
Wang, J.J.[Jia-Jun],
Chi, Z.[Zheru],
Iterative brain tumor retrieval for MR images based on user's
intention model,
PR(127), 2022, pp. 108650.
Elsevier DOI
2205
CBIR, Brain tumor images, Eye-tracking, Intention similarity,
Iterative retrieval, Relevance feedback
BibRef
Alpar, O.[Orcan],
Dolezal, R.[Rafael],
Ryska, P.[Pavel],
Krejcar, O.[Ondrej],
Nakagami-Fuzzy imaging framework for precise lesion segmentation in
MRI,
PR(128), 2022, pp. 108675.
Elsevier DOI
2205
Nakagami imaging, Fuzzy c-means, Lesion segmentation, MRI, BraTS
BibRef
Fiaz, K.[Kiran],
Madni, T.M.[Tahir Mustafa],
Anwar, F.[Fozia],
Janjua, U.I.[Uzair Iqbal],
Rafi, A.[Asra],
Abid, M.M.N.[Mian Muhammad Naeem],
Sultana, N.[Nasira],
Brain tumor segmentation and multiview multiscale-based radiomic
model for patient's overall survival prediction,
IJIST(32), No. 3, 2022, pp. 982-999.
DOI Link
2205
brain tumor segmentation, glioblastoma, MRI,
radiomic feature extraction, survival prediction
BibRef
Liu, F.[Fan],
Li, D.X.[Dong-Xiao],
Jin, X.Y.[Xin-Yu],
Qiu, W.Y.[Wen-Yuan],
Accelerated brain tumor dynamic contrast-enhanced MRI using Adaptive
Pharmaco-Kinetic Model Constrained method,
IJIST(32), No. 3, 2022, pp. 728-739.
DOI Link
2205
accelerated DCE-MRI, adaptive dictionary, brain tumor,
compressed sensing, Pharmacokinetic modeling
BibRef
Cui, S.G.[Shao-Guo],
Wei, M.J.[Ming-Jun],
Liu, C.[Chang],
Jiang, J.F.[Jing-Feng],
GAN-segNet: A deep generative adversarial segmentation network for
brain tumor semantic segmentation,
IJIST(32), No. 3, 2022, pp. 857-868.
DOI Link
2205
autoencoder, brain tumor, generative adversarial network,
label imbalance, semantic segmentation
BibRef
Ezhov, I.[Ivan],
Mot, T.[Tudor],
Shit, S.[Suprosanna],
Lipkova, J.[Jana],
Paetzold, J.C.[Johannes C.],
Kofler, F.[Florian],
Pellegrini, C.[Chantal],
Kollovieh, M.[Marcel],
Navarro, F.[Fernando],
Li, H.W.[Hong-Wei],
Metz, M.[Marie],
Wiestler, B.[Benedikt],
Menze, B.[Bjoern],
Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain
Tumor Models,
MedImg(41), No. 5, May 2022, pp. 1269-1278.
IEEE DOI
2205
Tumors, Numerical models, Computational modeling, Brain modeling,
Mathematical models, Biological system modeling, Bayes methods,
FET-PET
BibRef
Paul, R.[Rahul],
Topological features in addition to radiomics signature predict 1P19Q
status and tumor grade in low-grade gliomas,
IJIST(32), No. 3, 2022, pp. 753-766.
DOI Link
2205
1p19q codeletion, low grade glioma, radiomics,
topological data analysis, topological features, tumor grade
BibRef
Shivaprasad, B.J.,
Ravikumar, M.,
Guru, D.S.,
Analysis of Brain Tumor Using MR Images: A Brief Survey,
IJIG(22), No. 2, April 2022, pp. 2250023.
DOI Link
2205
BibRef
Zhou, T.X.[Tong-Xue],
Vera, P.[Pierre],
Canu, S.[Stéphane],
Ruan, S.[Su],
Missing Data Imputation via Conditional Generator and Correlation
Learning for Multimodal Brain Tumor Segmentation,
PRL(158), 2022, pp. 125-132.
Elsevier DOI
2205
Brain tumor segmentation, Conditional generator,
Correlation learning, Missing data, Multimodal fusion
BibRef
Cheng, J.H.[Jian-Hong],
Liu, J.[Jin],
Kuang, H.[Hulin],
Wang, J.X.[Jian-Xin],
A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma
Segmentation and IDH Genotyping,
MedImg(41), No. 6, June 2022, pp. 1520-1532.
IEEE DOI
2206
Image segmentation, Multitasking, Task analysis,
Magnetic resonance imaging, Feature extraction, Tumors,
transformer
BibRef
Gore, S.[Sonal],
Jagtap, J.[Jayant],
IDH-Based Radiogenomic Characterization of Glioma Using Local Ternary
Pattern Descriptor Integrated with Radiographic Features and Random
Forest Classifier,
IJIG(22), No. 3 2022, pp. 2140013.
DOI Link
2206
BibRef
Ismail, M.[Marwa],
Prasanna, P.[Prateek],
Bera, K.[Kaustav],
Statsevych, V.[Volodymyr],
Hill, V.[Virginia],
Singh, G.[Gagandeep],
Partovi, S.[Sasan],
Beig, N.[Niha],
McGarry, S.[Sean],
Laviolette, P.[Peter],
Ahluwalia, M.[Manmeet],
Madabhushi, A.[Anant],
Tiwari, P.[Pallavi],
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor
to Characterize Tumor Field Effect: Application to Survival
Prediction in Glioblastoma,
MedImg(41), No. 7, July 2022, pp. 1764-1777.
IEEE DOI
2207
Tumors, Strain, Feature extraction, Training, Radiomics,
Magnetic resonance imaging, Cancer, Glioblastoma, survival,
LASSO
BibRef
Liu, Y.[Yu],
Mu, F.[Fuhao],
Shi, Y.[Yu],
Chen, X.[Xun],
SF-Net: A Multi-Task Model for Brain Tumor Segmentation in Multimodal
MRI via Image Fusion,
SPLetters(29), 2022, pp. 1799-1803.
IEEE DOI
2209
Image segmentation, Tumors, Task analysis, Multitasking,
Brain modeling, Image fusion, Training, Brain tumor segmentation,
multi-task learning
BibRef
Polat, Ö.[Özlem],
Dokur, Z.[Zümray],
Ölmez, T.[Tamer],
Brain tumor classification by using a novel convolutional neural
network structure,
IJIST(32), No. 5, 2022, pp. 1646-1660.
DOI Link
2209
brain tumors, classification, convolutional neural networks,
divergence analysis, pattern recognition
BibRef
Asthana, P.[Pallavi],
Hanmandlu, M.[Madasu],
Vashisth, S.[Sharda],
Brain tumor detection and patient survival prediction using U-Net and
regression model,
IJIST(32), No. 5, 2022, pp. 1801-1814.
DOI Link
2209
biomedical imaging, brain tumor, deep learning, learning model,
regression model, segmentation
BibRef
Yang, Q.S.[Qiu-Shi],
Guo, X.Q.[Xiao-Qing],
Chen, Z.[Zhen],
Woo, P.Y.M.[Peter Y. M.],
Yuan, Y.X.[Yi-Xuan],
D2-Net: Dual Disentanglement Network for Brain Tumor Segmentation
With Missing Modalities,
MedImg(41), No. 10, October 2022, pp. 2953-2964.
IEEE DOI
2210
Tumors, Image segmentation, Magnetic resonance imaging,
Feature extraction, Correlation, Codes, Brain modeling, missing modalities
BibRef
Xiao, A.[Anqi],
Shen, B.[Biluo],
Shi, X.J.[Xiao-Jing],
Zhang, Z.[Zhe],
Zhang, Z.[Zeyu],
Tian, J.[Jie],
Ji, N.[Nan],
Hu, Z.H.[Zhen-Hua],
Intraoperative Glioma Grading Using Neural Architecture Search and
Multi-Modal Imaging,
MedImg(41), No. 10, October 2022, pp. 2570-2581.
IEEE DOI
2210
Imaging, Fluorescence, Feature extraction, Surgery,
Biomedical imaging, Medical diagnostic imaging, Deep learning,
NIR-II fluorescence imaging
BibRef
Joseph, S.S.[Sushitha Susan],
Dennisan, A.[Aju],
An affinity propagated clustering aided computerized Inherent Seeded
Region Growing and Deep learned Marching Cubes Algorithm (ISRG-DMCA)
based three dimensional image reconstruction approach,
IJIST(32), No. 6, 2022, pp. 2240-2254.
DOI Link
2212
3D reconstruction technique, brain tumor,
deep learned marching cubes algorithm, Shapelets
BibRef
Lather, M.[Mansi],
Singh, P.[Parvinder],
DDVM: dual decision voting mechanism for brain tumour identification
with LBP2Q-SVM type classifier,
IJCVR(13), No. 1, 2023, pp. 52-72.
DOI Link
2212
BibRef
Ghahramani, M.[Marzieh],
Shiri, N.[Nabiollah],
Brain tumour detection in magnetic resonance imaging using
Levenberg-Marquardt backpropagation neural network,
IET-IPR(17), No. 1, 2023, pp. 88-103.
DOI Link
2301
BibRef
Kaur, G.[Gurinderjeet],
Rana, P.S.[Prashant Singh],
Arora, V.[Vinay],
Deep learning and machine learning-based early survival predictions
of glioblastoma patients using pre-operative three-dimensional brain
magnetic resonance imaging modalities,
IJIST(33), No. 1, 2023, pp. 340-361.
DOI Link
2301
3D magnetic resonance imaging, brain tumor segmentation,
convolutional neural network, deep learning, machine learning, UNet
BibRef
Khosravanian, A.[Asieh],
Rahmanimanesh, M.[Mohammad],
Keshavarzi, P.[Parviz],
Mozaffari, S.[Saeed],
Enhancing level set brain tumor segmentation using fuzzy shape prior
information and deep learning,
IJIST(33), No. 1, 2023, pp. 323-339.
DOI Link
2301
brain tumor segmentation, deep learning,
fuzzy C-means clustering, level set method, shape prior information
BibRef
Huang, T.[Tongyuan],
Liu, Y.[Yao],
Research on the magnetic resonance imaging brain tumor segmentation
algorithm based on DO-UNet,
IJIST(33), No. 1, 2023, pp. 143-157.
DOI Link
2301
attention mechanism, brain tumor segmentation,
deep over-parameterized convolution, multi-scale features, U-net model
BibRef
Li, Z.W.[Zi-Wei],
Xuan, S.B.[Shi-Bin],
He, X.D.[Xue-Dong],
Wang, L.[Li],
Global weighted average pooling network with multilevel feature
fusion for weakly supervised brain tumor segmentation,
IET-IPR(17), No. 2, 2023, pp. 418-427.
DOI Link
2302
BibRef
Song, X.F.[Xiao-Fan],
Li, J.[Jun],
Qian, X.H.[Xiao-Hua],
Diagnosis of Glioblastoma Multiforme Progression via Interpretable
Structure-Constrained Graph Neural Networks,
MedImg(42), No. 2, February 2023, pp. 380-390.
IEEE DOI
2302
Task analysis, Feature extraction, Graph neural networks,
Adaptation models, Predictive models,
interpretability
BibRef
Hussain, S.S.[Syed Sajid],
Sachdeva, J.[Jainy],
Ahuja, C.K.[Chirag Kamal],
Singh, A.[Abhiav],
Enc-Unet: A novel method for Glioma segmentation,
IJIST(33), No. 2, 2023, pp. 465-482.
DOI Link
2303
3D-Unet, autoencoders, deep learning, glioma segmentation, MRI
BibRef
Gudigar, A.[Anjan],
Raghavendra, U.,
Rao, T.N.[Tejaswi N.],
Samanth, J.[Jyothi],
Rajinikanth, V.[Venkatesan],
Satapathy, S.C.[Suresh Chandra],
Ciaccio, E.J.[Edward J.],
Yee, C.W.[Chan Wai],
Acharya, U.R.[U. Rajendra],
FFCAEs: An efficient feature fusion framework using cascaded
autoencoders for the identification of gliomas,
IJIST(33), No. 2, 2023, pp. 483-494.
DOI Link
2303
cascaded autoencoders, classification accuracy,
computer-aided diagnostic tool, feature fusion framework,
magnetic resonance (MR)
BibRef
Kavitha, A.R.[Angamuthu Rajasekaran],
Palaniappan, K.[Karthikeyan],
Brain tumor segmentation using a deep Shuffled-YOLO network,
IJIST(33), No. 2, 2023, pp. 511-522.
DOI Link
2303
brain tumor, multi-modalities,
scalable range-based adaptive bilateral filter, segmentation,
Shuffled-YOLO network
BibRef
Khan, M.A.[Muhammad Attique],
Khan, A.[Awais],
Alhaisoni, M.[Majed],
Alqahtani, A.[Abdullah],
Alsubai, S.[Shtwai],
Alharbi, M.[Meshal],
Malik, N.A.[Nazir Ahmed],
Damaševicius, R.[Robertas],
Multimodal brain tumor detection and classification using deep
saliency map and improved dragonfly optimization algorithm,
IJIST(33), No. 2, 2023, pp. 572-587.
DOI Link
2303
brain tumor, contrast enhancement, deep learning, fusion,
improved dragon fly optimization, MRI
BibRef
Ragupathy, B.[Balakumaresan],
Subramani, B.[Bharath],
Arumugam, S.[Selvapandian],
A novel approach for MR brain tumor classification and detection
using optimal CNN-SVM model,
IJIST(33), No. 2, 2023, pp. 746-759.
DOI Link
2303
anisotropic diffusion, brain tumor,
convolutional neural network, morphological operations, support vector machine
BibRef
Raju, A.R.[Ayalapogu Ratna],
Pabboju, S.[Suresh],
Ramisetty, R.R.[Rajeswara Rao],
Performance Analysis and Critical Review on Segmentation Techniques for
Brain Tumor Classification,
IJIG(23), No. 2 2023, pp. 2350023.
DOI Link
2303
BibRef
Subramanian, S.[Shashank],
Ghafouri, A.[Ali],
Scheufele, K.M.[Klaudius Matthias],
Himthani, N.[Naveen],
Davatzikos, C.[Christos],
Biros, G.[George],
Ensemble Inversion for Brain Tumor Growth Models With Mass Effect,
MedImg(42), No. 4, April 2023, pp. 982-995.
IEEE DOI
2304
Tumors, Brain modeling, Biological system modeling,
Mathematical models, Calibration, Integrated circuit modeling, glioblastoma
BibRef
Cinar, N.[Necip],
Kaya, M.[Mehmet],
Kaya, B.[Buket],
A novel convolutional neural network-based approach for brain tumor
classification using magnetic resonance images,
IJIST(33), No. 3, 2023, pp. 895-908.
DOI Link
2305
artificial neural network models, brain tumor classification,
convolutional neural network, deep learning
BibRef
Liu, Z.X.[Zeng-Xin],
Ma, C.W.[Cai-Wen],
She, W.J.[Wen-Ji],
Wang, X.[Xuan],
TransMVU: Multi-view 2D U-Nets with transformer for brain tumour
segmentation,
IET-IPR(17), No. 6, 2023, pp. 1874-1882.
DOI Link
2305
image segmentation, medical image processing, tumours
BibRef
Zhou, T.X.[Tong-Xue],
Feature fusion and latent feature learning guided brain tumor
segmentation and missing modality recovery network,
PR(141), 2023, pp. 109665.
Elsevier DOI
2306
Brain tumor segmentation, Multimodal feature fusion,
Missing modalities, Spatial consistency, Latent feature learning
BibRef
Malhotra, R.[Radhika],
Singh-Saini, B.[Barjinder],
Gupta, S.[Savita],
CB-D2RNet: An efficient context bridge network for glioma
segmentation,
JVCIR(94), 2023, pp. 103836.
Elsevier DOI
2306
MRI, Context bridge, Loss function, Dilated convolution
BibRef
Xue, J.[Jie],
Li, Q.[Qi],
Liu, X.[Xiyu],
Guo, Y.J.[Yu-Jie],
Lu, J.[Jie],
Song, B.[Bosheng],
Huang, P.[Pu],
An, Q.[Qiong],
Gong, G.Z.[Guan-Zhong],
Li, D.W.[Deng-Wang],
Hybrid neural-like P systems with evolutionary channels for multiple
brain metastases segmentation,
PR(142), 2023, pp. 109651.
Elsevier DOI
2307
Hybrid neural-like P system, Evolutionary channels,
Segmentation of brain metastases
BibRef
Akram, M.T.[Muhammad Tahir],
Asghar, S.[Sohail],
Shahid, A.R.[Ahmad Raza],
Effective data augmentation for brain tumor segmentation,
IJIST(33), No. 4, 2023, pp. 1247-1260.
DOI Link
2307
2D U-Nets, brain tumor segmentation, data augmentation, glioma segmentation
BibRef
Zhu, J.Y.[Jing-Yu],
Ye, J.M.[Jian-Ming],
Dong, L.[Leshui],
Ma, X.F.[Xiao-Fei],
Tang, N.[Na],
Xu, P.[Peng],
Jin, W.[Wei],
Li, R.P.[Rui-Peng],
Yang, G.[Guang],
Lai, X.B.[Xiao-Bo],
Non-invasive prediction of overall survival time for glioblastoma
multiforme patients based on multimodal MRI radiomics,
IJIST(33), No. 4, 2023, pp. 1261-1274.
DOI Link
2307
deep learning, glioblastoma multiforme,
magnetic resonance imaging, overall survival time, radiomics
BibRef
Kollem, S.[Sreedhar],
Reddy, K.R.[Katta Ramalinga],
Prasad, C.R.[Ch. Rajendra],
Chakraborty, A.[Avishek],
Ajayan, J.,
Sreejith, S.,
Bhattacharya, S.[Sandip],
Leo Joseph, L.M.I.,
Janapati, R.[Ravichander],
AlexNet-NDTL: Classification of MRI brain tumor images using modified
AlexNet with deep transfer learning and Lipschitz-based data
augmentation,
IJIST(33), No. 4, 2023, pp. 1306-1322.
DOI Link
2307
AlexNet, data augmentation, deep neural networks,
magnetic resonance imaging, transfer learning
BibRef
Nehru, V.,
Prabhu, V.,
Segmentation of brain tumor subregions with depthwise separable dense
U-NET (DSDU-NET),
IJIST(33), No. 4, 2023, pp. 1323-1334.
DOI Link
2307
brain tumor segmentation, depthwise separable convolutional networks,
whole tumor (WT)
BibRef
Lin, J.W.[Jian-Wei],
Lin, J.[Jiatai],
Lu, C.[Cheng],
Chen, H.[Hao],
Lin, H.[Huan],
Zhao, B.[Bingchao],
Shi, Z.W.[Zhen-Wei],
Qiu, B.J.[Bing-Jiang],
Pan, X.P.[Xi-Peng],
Xu, Z.[Zeyan],
Huang, B.[Biao],
Liang, C.H.[Chang-Hong],
Han, G.Q.[Guo-Qiang],
Liu, Z.[Zaiyi],
Han, C.[Chu],
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer With
Modality-Correlated Cross-Attention for Brain Tumor Segmentation,
MedImg(42), No. 8, August 2023, pp. 2451-2461.
IEEE DOI
2308
Transformers, Tumors, Brain modeling, Image segmentation,
Magnetic resonance imaging, Feature extraction, multi-modal fusion
BibRef
Wang, P.X.[Pei-Xu],
Liu, S.K.[Shi-Kun],
Peng, J.L.[Jia-Lin],
AST-Net: Lightweight Hybrid Transformer for Multimodal Brain Tumor
Segmentation,
ICPR22(4623-4629)
IEEE DOI
2212
Training, Image segmentation, Solid modeling,
Computational modeling,
Hybrid model
BibRef
De, A.[Arijit],
Mhatre, R.[Radhika],
Tiwari, M.[Mona],
Chowdhury, A.S.[Ananda S.],
Brain Tumor Classification from Radiology and Histopathology using
Deep Features and Graph Convolutional Network,
ICPR22(4420-4426)
IEEE DOI
2212
Histopathology, Convolution,
Magnetic resonance imaging, Radiology, Feature extraction,
Graph Convolution Network
BibRef
Zhou, T.X.[Tong-Xue],
Noeuveglise, A.[Alexandra],
Ghazouani, F.[Fethi],
Modzelewski, R.[Romain],
Thureau, S.[Sébastien],
Fontanilles, M.[Maxime],
Ruan, S.[Su],
Prediction of Brain Tumor Recurrence Location Based on
Kullback-Leibler Divergence and Nonlinear Correlation Learning,
ICPR22(4414-4419)
IEEE DOI
2212
Correlation, Transfer learning, Semantics, Surgery,
Feature extraction, Loss measurement, Planning
BibRef
Alksas, A.[Ahmed],
Shehata, M.[Mohamed],
Atef, H.[Hala],
Sherif, F.[Fatma],
Yaghi, M.[Maha],
Alhalabi, M.[Marah],
Ghazal, M.[Mohammed],
El Serougy, L.[Lamiaa],
El-Baz, A.[Ayman],
A Comprehensive Non-invasive System for Early Grading of Gliomas,
ICPR22(4371-4377)
IEEE DOI
2212
Histograms, Solid modeling, Sensitivity, Imaging, Magnetic resonance,
Artificial neural networks, Feature extraction, CAD, MRIs, HOG, GLCM, ADC
BibRef
Dutta, T.K.[Tapas Kumar],
Nayak, D.R.[Deepak Ranjan],
CDANet: Channel Split Dual Attention Based CNN for Brain Tumor
Classification In Mr Images,
ICIP22(4208-4212)
IEEE DOI
2211
Codes, Brain modeling, Convolutional neural networks, Lesions,
Cancer, Brain tumor classification, Channel split dual attention, CNN
BibRef
Ponikiewski, W.[Wojciech],
Nalepa, J.[Jakub],
Deep Learning Meets Radiomics For End-To-End Brain Tumor MRI Analysis,
ICIP22(1301-1305)
IEEE DOI
2211
Image segmentation, Magnetic resonance imaging,
Volume measurement, Pipelines, Manuals, Feature extraction, Lesions,
radiomics
BibRef
Andrade-Miranda, G.,
Jaouen, V.,
Bourbonne, V.,
Lucia, F.,
Visvikis, D.,
Conze, P.H.,
Pure Versus Hybrid Transformers For Multi-Modal Brain Tumor
Segmentation: A Comparative Study,
ICIP22(1336-1340)
IEEE DOI
2211
Image segmentation, Statistical analysis, Pipelines, Transformers,
Brain modeling, Data models, Robustness, Vision Transformers,
hybrid CNN-Transformers models
BibRef
Dequidt, P.[Paul],
Bourdon, P.[Pascal],
Tremblais, B.[Benoit],
Guillevin, C.[Carole],
Gianelli, B.[Benoit],
Boutet, C.[Claire],
Cottier, J.P.[Jean-Philippe],
Vallée, J.N.[Jean-Noël],
Fernandez-Maloigne, C.[Christine],
Guillevin, R.[Rémy],
Assigning a new glioma grade label ground-truth for the BraTS dataset
using radiologic criteria,
IPTA20(1-6)
IEEE DOI
2206
Sociology, Magnetic resonance, Organizations, Tools, Oncology,
Statistics, Tumors, Glioma grading, machine learning,
radiomics
BibRef
Brzenczek, C.[Cyril],
Mézières, S.[Sophie],
Gaudeau, Y.[Yann],
Blonski, M.[Marie],
Rech, F.[Fabien],
Obara, T.[Tiphaine],
Taillandier, L.[Luc],
Moureaux, J.M.[Jean-Marie],
An original MRI-based method to quantify the Diffuse Low-Grade Glioma
brain infiltration,
IPTA20(1-6)
IEEE DOI
2206
Magnetic resonance imaging, Standards organizations, Morphology,
Medical treatment, Organizations, Tools, Tumors, Brain,
Segmentation
BibRef
Ding, Y.H.[Yu-Hang],
Yu, X.[Xin],
Yang, Y.[Yi],
RFNet: Region-aware Fusion Network for Incomplete Multi-modal Brain
Tumor Segmentation,
ICCV21(3955-3964)
IEEE DOI
2203
Training, Degradation, Image segmentation, Sensitivity,
Magnetic resonance imaging, Aggregates, Medical, biological,
BibRef
Sagar, A.[Abhinav],
Uncertainty Quantification using Variational Inference for Biomedical
Image Segmentation,
VAQuality22(44-51)
IEEE DOI
2202
Weight measurement, Image segmentation, Uncertainty,
Brain modeling, Time measurement, Decoding, Bayes methods
BibRef
Abolvardi, A.A.[Ava Assadi],
Hamey, L.[Len],
Ho-Shon, K.[Kevin],
UNET-Based Multi-Task Architecture for Brain Lesion Segmentation,
DICTA20(1-7)
IEEE DOI
2201
Deep learning, Training, Image segmentation, Lesions, Task analysis,
Biomedical imaging, Deep Learning, Multi-task learning
BibRef
Li, X.[Xuhui],
Xu, Y.[Yong],
Xiang, F.[Feng],
Liu, Q.[Qing],
Huang, W.H.[Wei-Hong],
Xie, B.[Bin],
KINET: A Non-Invasive Method for Predicting Ki67 Index of Glioma,
ICIP21(150-154)
IEEE DOI
2201
Deep learning, Training, Magnetic resonance imaging, Surgery,
Medical services, Metadata, Ki67 index, KiNet, multi-modal, glioma, MRI
BibRef
Nguyen, T.H.[Thanh Hau],
Le, C.H.[Cong Hau],
Sang, D.V.[Dinh Viet],
Yao, T.T.[Ting-Ting],
Li, W.[Wei],
Wang, Z.Y.[Zhi-Yong],
Efficient Brain Tumor Segmentation with Dilated Multi-fiber Network
and Weighted Bi-directional Feature Pyramid Network,
DICTA20(1-7)
IEEE DOI
2201
Deep learning, Computer architecture, Bidirectional control,
Network architecture, Tumors, Cancer, Software development management
BibRef
Cirillo, M.D.[Marco Domenico],
Abramian, D.[David],
Eklund, A.[Anders],
What is the Best Data Augmentation for 3D Brain Tumor Segmentation?,
ICIP21(36-40)
IEEE DOI
2201
Training, Image segmentation, Brightness, Standards, Tumors,
Data augmentation, 3D brain tumor segmentation, MRI, 3D U-Net,
artificial intelligence
BibRef
Le, N.[Ngan],
Yamazaki, K.[Kashu],
Quach, K.G.[Kha Gia],
Truong, D.[Dat],
Savvides, M.[Marios],
A Multi-task Contextual Atrous Residual Network for Brain Tumor
Detection Segmentation,
ICPR21(5943-5950)
IEEE DOI
2105
Measurement, Image segmentation,
Convolution, Brain modeling, Proposals, Kernel
BibRef
Ji, Z.,
Han, X.,
Lin, T.,
Wang, W.,
A Dense-Gated U-Net for Brain Lesion Segmentation,
VCIP20(104-107)
IEEE DOI
2102
biomedical MRI, brain, diseases, feature extraction,
image segmentation, medical image processing, tumours,
dense gates
BibRef
Nwe, T.L.,
Min, O.Z.,
Gopalakrishnan, S.,
Lin, D.,
Prasad, S.,
Dong, S.,
Li, Y.,
Pahwa, R.S.,
Improving 3D Brain Tumor Segmentation With Predict-Refine Mechanism
Using Saliency And Feature Maps,
ICIP20(2671-2675)
IEEE DOI
2011
Tumors, Image segmentation,
Magnetic resonance imaging, Training, Brain modeling,
Brain tumor Segmentation
BibRef
El Kaitouni, S.E.I.,
Tairi, H.,
Segmentation of medical images for the extraction of brain tumors:
A comparative study between the Hidden Markov and Deep Learning approaches,
ISCV20(1-5)
IEEE DOI
2011
biomedical MRI, brain, feature extraction, image classification,
image segmentation, learning (artificial intelligence),
Medical images.
BibRef
Kong, L.W.[Ling-Wei],
Zhang, Y.F.[Yi-Feng],
Multi-modal Brain Tumor Segmentation Using Cascaded 3D U-Net,
ICIVC21(129-133)
IEEE DOI
2112
Training, Degradation, Image segmentation,
Magnetic resonance imaging, weighted Tversky loss
BibRef
Li, K.,
Kong, L.W.[Ling-Wei],
Zhang, Y.F.[Yi-Feng],
3D U-Net Brain Tumor Segmentation Using VAE Skip Connection,
ICIVC20(97-101)
IEEE DOI
2009
Image segmentation, Tumors, Decoding,
Magnetic resonance imaging, Semantics, Computer architecture, ShakeDrop
BibRef
Xi, N.,
Semi-supervised Attentive Mutual-info Generative Adversarial Network
for Brain Tumor Segmentation,
IVCNZ19(1-7)
IEEE DOI
2004
biomedical MRI, brain, image segmentation, mutual information,
learning (artificial intelligence), medical image processing
BibRef
Liu, S.[Sun'ao],
Xu, H.[Hai],
Liu, Y.Z.[Yi-Zhi],
Xie, H.T.[Hong-Tao],
Improving Brain Tumor Segmentation with Dilated Pseudo-3d Convolution
and Multi-direction Fusion,
MMMod20(I:727-738).
Springer DOI
2003
BibRef
Jia, Z.D.[Zhong-Dao],
Yuan, Z.M.[Zhi-Min],
Peng, J.L.[Jia-Lin],
Multimodal Brain Tumor Segmentation Using Encoder-decoder with
Hierarchical Separable Convolution,
MBIA19(130-138).
Springer DOI
1912
BibRef
Liu, H.Y.[Hong-Ying],
Shen, X.J.[Xiong-Jie],
Shang, F.H.[Fan-Hua],
Ge, F.H.[Fei-Hang],
Wang, F.[Fei],
Cu-net: Cascaded U-net with Loss Weighted Sampling for Brain Tumor
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MBIA19(102-111).
Springer DOI
1912
BibRef
Nalepa, J.,
Mrukwa, G.,
Piechaczek, S.,
Lorenzo, P.R.,
Marcinkiewicz, M.,
Bobek-Billewicz, B.,
Wawrzyniak, P.,
Ulrych, P.,
Szymanek, J.,
Cwiek, M.,
Dudzik, W.,
Kawulok, M.,
Hayball, M.P.,
Data Augmentation via Image Registration,
ICIP19(4250-4254)
IEEE DOI
1910
Deep learning, data augmentation, image registration,
brain-tumor segmentation
BibRef
Sun, Y.,
Zhou, C.,
Fu, Y.,
Xue, X.,
Parasitic GAN for Semi-Supervised Brain Tumor Segmentation,
ICIP19(1535-1539)
IEEE DOI
1910
Generative adversarial networks, medical image processing, volume segmentation
BibRef
Zhao, H.,
Guo, Y.,
Zheng, Y.,
A Compound Neural Network for Brain Tumor Segmentation,
ICIP19(1435-1439)
IEEE DOI
1910
Convolutional Neural Network, Brain Tumor Segmentation,
Magnetic Resonance Imaging, Automated System, Features Extracting
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Ali, M.B.[Muhaddisa Barat],
Gu, I.Y.H.[Irene Yu-Hua],
Jakola, A.S.[Asgeir Store],
Multi-stream Convolutional Autoencoder and 2D Generative Adversarial
Network for Glioma Classification,
CAIP19(I:234-245).
Springer DOI
1909
BibRef
Talamonti, C.[Cinzia],
Piffer, S.[Stefano],
Greto, D.[Daniela],
Mangoni, M.[Monica],
Ciccarone, A.[Antonio],
Dicarolo, P.[Paolo],
Fantacci, M.E.[Maria Evelina],
Fusi, F.[Franco],
Oliva, P.[Piernicola],
Palumbo, L.[Letizia],
Favre, C.[Claudio],
Livi, L.[Lorenzo],
Pallotta, S.[Stefania],
Retico, A.[Alessandra],
Radiomic and Dosiomic Profiling of Paediatric Medulloblastoma Tumours
Treated with Intensity Modulated Radiation Therapy,
CAIPWS19(56-64).
Springer DOI
1909
BibRef
Abd-Ellah, M.K.[Mahmoud Khaled],
Khalaf, A.A.M.[Ashraf A. M.],
Awad, A.I.[Ali Ismail],
Hamed, H.F.A.[Hesham F. A.],
TPUAR-Net: Two Parallel U-Net with Asymmetric Residual-Based Deep
Convolutional Neural Network for Brain Tumor Segmentation,
ICIAR19(II:106-116).
Springer DOI
1909
BibRef
Cui, S.[Siming],
Shen, X.[Xuanjing],
Lyu, Y.[Yingda],
Automatic Segmentation of Brain Tumor Image Based on Region Growing
with Co-constraint,
MMMod19(I:603-615).
Springer DOI
1901
BibRef
Dhara, A.K.,
Arvids, E.,
Fahlström, M.,
Wikström, J.,
Larsson, E.,
Strand, R.,
Interactive Segmentation of Glioblastoma for Post-surgical Treatment
Follow-up,
ICPR18(1199-1204)
IEEE DOI
1812
Image segmentation, Tumors, Magnetic resonance imaging, Training,
Surgery, Tools, Convolution
BibRef
Chen, X.[Xuan],
Liew, J.H.[Jun Hao],
Xiong, W.[Wei],
Chui, C.K.[Chee-Kong],
Ong, S.H.[Sim-Heng],
Focus, Segment and Erase:
An Efficient Network for Multi-label Brain Tumor Segmentation,
ECCV18(XIII: 674-689).
Springer DOI
1810
BibRef
Zhang, L.[Lichi],
Zhang, H.[Han],
Rekik, I.[Islem],
Gao, Y.Z.[Yao-Zong],
Wang, Q.[Qian],
Shen, D.G.[Ding-Gang],
Malignant Brain Tumor Classification Using the Random Forest Method,
SSSPR18(14-21).
Springer DOI
1810
BibRef
Naudin, M.[Mathieu],
Tremblais, B.[Benoit],
Guillevin, C.[Carole],
Guillevin, R.[Rémy],
Fernandez-Maloigne, C.[Christine],
Diffuse Low Grade Glioma NMR Assessment for Better Intra-operative
Targeting Using Fuzzy Logic,
ACIVS18(200-210).
Springer DOI
1810
BibRef
Ge, C.,
Qu, Q.,
Gu, I.Y.,
Store Jakola, A.,
3D Multi-Scale Convolutional Networks for Glioma Grading Using MR
Images,
ICIP18(141-145)
IEEE DOI
1809
Tumors, Magnetic resonance imaging,
Training, Feature extraction, Machine learning,
MRIs
BibRef
Bousselham, A.,
Bouattane, O.,
Youssfi, M.,
Raihani, A.,
Thermal effect analysis of brain tumor on simulated T1-weighted MRI
images,
ISCV18(1-6)
IEEE DOI
1807
biomedical MRI, biothermics, brain, finite difference methods,
medical image processing, spin-lattice relaxation,
finite difference method
BibRef
Shen, H.,
Zhang, J.,
Zheng, W.,
Efficient symmetry-driven fully convolutional network for multimodal
brain tumor segmentation,
ICIP17(3864-3868)
IEEE DOI
1803
Convolutional codes, Image segmentation, Task analysis,
Training, Tumors,
brain tumor segmentation
BibRef
Shen, H.,
Zhang, J.,
Fully connected CRF with data-driven prior for multi-class brain
tumor segmentation,
ICIP17(1727-1731)
IEEE DOI
1803
biomedical MRI, brain, image segmentation,
learning (artificial intelligence), medical image processing,
prior
BibRef
Murthy, V.,
Hou, L.,
Samaras, D.,
Kurc, T.M.,
Saltz, J.H.,
Center-Focusing Multi-task CNN with Injected Features for
Classification of Glioma Nuclear Images,
WACV17(834-841)
IEEE DOI
1609
Decision support systems, Handheld, computers
BibRef
Urien, H.[Hélène],
Buvat, I.[Irène],
Rougon, N.[Nicolas],
Soussan, M.[Michaël],
Bloch, I.[Isabelle],
Brain Lesion Detection in 3D PET Images Using Max-Trees and a New
Spatial Context Criterion,
ISMM17(455-466).
Springer DOI
1706
BibRef
Bento, M.[Mariana],
Sym, Y.[Yan],
Frayne, R.[Richard],
Lotufo, R.[Roberto],
Rittner, L.[Letícia],
Probabilistic Segmentation of Brain White Matter Lesions Using
Texture-Based Classification,
ICIAR17(71-78).
Springer DOI
1706
BibRef
Salvador, R.,
Fabelo, H.,
Lazcano, R.,
Ortega, S.,
Madroñal, D.,
Callicó, G.M.,
Juárez, E.,
Sanz, C.,
Demo: HELICoiD tool demonstrator for real-time brain cancer detection,
DASIP16(237-238)
IEEE DOI
1704
biological tissues
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Mukherjee, S.[Sabyasachi],
Bandyopadhyay, O.[Oishila],
Biswas, A.[Arindam],
Automated Brain Tumor Diagnosis and Severity Analysis from Brain MRI,
CompIMAGE16(194-207).
Springer DOI
1704
BibRef
Réjichi, S.,
Chaabane, F.,
Brain tumor extraction using graph based classification of MRI time
series for diagnostic assistance,
ISIVC16(320-324)
IEEE DOI
1704
Feature extraction
BibRef
Jaroudi, R.[Rym],
Baravdish, G.[George],
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Johansson, B.T.[B. Tomas],
Source Localization of Reaction-Diffusion Models for Brain Tumors,
GCPR16(414-425).
Springer DOI
1611
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Dvorák, P.[Pavel],
Menze, B.H.[Bjoern H.],
Local Structure Prediction with Convolutional Neural Networks for
Multimodal Brain Tumor Segmentation,
MCV15(59-71).
Springer DOI
1608
BibRef
Swiderska, Z.[Zaneta],
Markiewicz, T.[Tomasz],
Grala, B.[Bartlomiej],
Kozlowski, W.[Wojciech],
Texture and Mathematical Morphology for Hot-Spot Detection in Whole
Slide Images of Meningiomas and Oligodendrogliomas,
CAIP15(II:1-12).
Springer DOI
1511
BibRef
de Marsico, M.[Maria],
Nappi, M.[Michele],
Riccio, D.[Daniel],
Entropy-Based Automatic Segmentation and Extraction of Tumors from
Brain MRI Images,
CAIP15(II:195-206).
Springer DOI
1511
BibRef
Pedoia, V.[Valentina],
Balbi, S.[Sergio],
Binaghi, E.[Elisabetta],
Fully Automatic Brain Tumor Segmentation by Using Competitive EM and
Graph Cut,
CIAP15(I:568-578).
Springer DOI
1511
BibRef
Roy, S.[Shaswati],
Maji, P.[Pradipta],
A New Post-processing Method to Detect Brain Tumor Using Rough-Fuzzy
Clustering,
PReMI15(407-417).
Springer DOI
1511
BibRef
Oh, K.H.[Kang Han],
Kim, S.H.[Soo Hyung],
Lee, M.[Myungeun],
Tumor detection on brain MR images using regional features: Method
and preliminary results,
FCV15(1-4)
IEEE DOI
1506
biomedical MRI
BibRef
Cabria, I.[Ivan],
Gondra, I.[Iker],
Automated Localization of Brain Tumors in MRI Using Potential-K-Means
Clustering Algorithm,
CRV15(125-132)
IEEE DOI
1507
Biomedical imaging
BibRef
Martinez-Cortes, T.[Tomas],
Fernandez-Torres, M.A.[Miguel Angel],
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Guzman-De-Villoria, J.A.[Juan Adan],
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A Bayesian model for brain tumor classification using clinical-based
features,
ICIP14(2779-2783)
IEEE DOI
1502
Bayes methods
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Al-Shaikhli, S.D.S.[Saif Dawood Salman],
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Brain tumor classification using sparse coding and dictionary
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ICIP14(2774-2778)
IEEE DOI
1502
Brain
BibRef
And:
Coupled Dictionary Learning for Automatic Multi-Label Brain Tumor
Segmentation in Flair MRI images,
ISVC14(I: 489-500).
Springer DOI
1501
BibRef
Havaei, M.[Mohammad],
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Larochelle, H.[Hugo],
Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN
Classification,
ICPR14(556-561)
IEEE DOI
1412
Brain
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Zhou, M.[Mu],
Hall, L.O.[Lawrence O.],
Goldgof, D.B.[Dmitry B.],
Exploring Brain Tumor Heterogeneity for Survival Time Prediction,
ICPR14(580-585)
IEEE DOI
1412
Accuracy
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Subbanna, N.[Nagesh],
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Iterative Multilevel MRF Leveraging Context and Voxel Information for
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CVPR14(400-405)
IEEE DOI
1409
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A Parallel Adaptive Physics-Based Non-rigid Registration Framework for
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1407
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Brain Tumor Classification in MRI Scans Using Sparse Representation,
ICISP14(629-637).
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1406
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WSSIP14(47-50)
1406
Atmospheric measurements
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1208
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1011
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1008
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An EM algorithm for brain tumor image registration:
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MMBIA10(39-46).
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1006
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Graph cut segmentation technique for MRI brain tumor extraction,
IPTA10(284-287).
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1007
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Automated Segmentation of Brain Tumors in MRI Using Force Data
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ISVC09(I: 317-326).
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0910
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Encephalic NMR Tumor Diversification by Textural Interpretation,
CIAP09(394-403).
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0909
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Machucho-Cadena, R.[Ruben],
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Cortex, Alzheimer's Disease, Dementia .