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Code, Brain Lesion Segmentation. multi-scale 3D Deep Convolutional Neural Network coupled with a 3D
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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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SVM; Segmentation; Feature selection; Fusion; Follow-up system; Brain
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ICIP14(1887-1891)
IEEE DOI
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Feature extraction
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1203
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Automatic detection of brain tumor in magnetic resonance images
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IJIST(23), No. 2, 2013, pp. 97-103.
DOI Link
1307
support vector machine, radial basis function, binarized image
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DOI Link
1403
tumor
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Incorporating prior information in the fuzzy C-mean algorithm with
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Chih-Feng, C.[Chen],
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Chun-Chung, L.[Lui],
Chen-Chang, L.[Lee],
Hsu-Huei, W.[Weng],
Yuan-Hsiung, T.[Tsai],
Ho-Ling, L.[Liu],
In vivo correlation between semi-quantitative hemodynamic parameters
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IJIST(22), No. 2, June 2012, pp. 132-136.
DOI Link
1202
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Angoth, V.[Vivek],
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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.
DOI Link
1303
BibRef
Dhanalakshmi, K.,
Rajamani, V.,
An intelligent mining system for diagnosing medical images
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IJIST(23), No. 2, 2013, pp. 194-203.
DOI Link brain tumor, image processing, association rule mining, associative classifier
1307
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Arakeri, M.P.[Megha P.],
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An intelligent content-based image retrieval system for clinical
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1308
Discriminant Convex Non-negative Matrix Factorization
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Beno, M.M.[M. Marsaline],
Valarmathi, I.R.,
Swamy, S.M.,
Rajakumar, B.R.,
Threshold prediction for segmenting tumour from brain MRI scans,
IJIST(24), No. 2, 2014, pp. 129-137.
DOI Link
1405
bilateral filter
BibRef
Kwon, D.J.[Dong-Jin],
Niethammer, M.,
Akbari, H.,
Bilello, M.,
Davatzikos, C.,
Pohl, K.M.,
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MedImg(33), No. 3, March 2014, pp. 651-667.
IEEE DOI
1404
biomedical MRI
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Govindaraj, V.[Vishnuvarthanan],
Murugan, P.R.[Pallikonda Rajasekaran],
A complete automated algorithm for segmentation of tissues and
identification of tumor region in T1, T2, and FLAIR brain images
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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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DOI Link
1506
MRI images
BibRef
Militello, C.[Carmelo],
Rundo, L.[Leonardo],
Vitabile, S.[Salvatore],
Russo, G.[Giorgio],
Pisciotta, P.[Pietro],
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Ippolito, M.[Massimo],
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1509
semi-automatic segmentation
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Stefano, A.[Alessandro],
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Menze, B.H.,
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Slotboom, J.,
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Lanczi, L.,
Gerstner, E.,
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Arbel, T.,
Avants, B.B.,
Ayache, N.,
Buendia, P.,
Collins, D.L.,
Cordier, N.,
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Das, T.,
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Demiralp, C.,
Durst, C.R.,
Dojat, M.,
Doyle, S.,
Festa, J.,
Forbes, F.,
Geremia, E.,
Glocker, B.,
Golland, P.,
Guo, X.[Xiaotao],
Hamamci, A.,
Iftekharuddin, K.M.,
Jena, R.,
John, N.M.,
Konukoglu, E.,
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Mariz, J.A.,
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Raviv, T.R.[T. Riklin],
Reza, S.M.S.,
Ryan, M.,
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Shotton, J.,
Silva, C.A.,
Sousa, N.,
Subbanna, N.K.,
Szekely, G.,
Taylor, T.J.,
Thomas, O.M.,
Tustison, N.J.,
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IEEE DOI
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benchmark testing
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DOI Link
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enhancement
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1601
biomedical MRI
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1604
Biomedical image processing
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Xiao, K.[Kai],
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ICIG11(297-302).
IEEE DOI
1109
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Stefano, A.[Alessandro],
Vitabile, S.[Salvatore],
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Ippolito, M.[Massimo],
Marletta, F.[Franco],
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d'Urso, D.[Davide],
Gambino, O.[Orazio],
Pirrone, R.[Roberto],
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A fully automatic method for biological target volume segmentation of
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DOI Link
1604
random walk
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Alves, V.,
Silva, C.A.,
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI
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MedImg(35), No. 5, May 2016, pp. 1240-1251.
IEEE DOI
1605
Brain modeling
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Thirumurugan, P.,
Ramkumar, D.,
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Automated detection of glioblastoma tumor in brain magnetic imaging
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DOI Link
1606
contourlet transform
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Kathirvel, R.,
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A computer-aided approach for meningioma brain tumor detection using
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DOI Link
1708
accuracy, brain tumor, classifier, contourlet transform, , features
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Kathirvel, R.,
Batri, K.,
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DOI Link
1708
brain tumors, detection, diagnosis, features, , tissues
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Thirumurugan, P.,
Shanthakumar, P.,
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IJIST(26), No. 2, 2016, pp. 157-162.
DOI Link
1606
gray matter, white matter, CSF, brain tumor, brain tissue
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Kumarganesh, S.,
Suganthi, M.,
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1701
brain tumor
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Thiruvenkadam, K.[Kalaiselvi],
Perumal, N.[Nagaraja],
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.,
Rajasekaran, M.P.[M. Pallikonda],
Vishnuvarthanan, N.A.[N. Anitha],
Prasath, T.A.[T. Arun],
Kannan, M.,
Tumor detection in T1, T2, FLAIR and MPR brain images using a
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DOI Link
1704
MPSO-based FCM
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Angulakshmi, M.,
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1704
review
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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],
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Adaptive stochastic segmentation via energy-convergence for brain
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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
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IET-IPR(11), No. 8, August 2017, pp. 620-632.
DOI Link
1708
BibRef
Ramakrishnan, T.,
Sankaragomathi, B.,
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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
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.[Zhiguang],
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
gradient boosting classifier,
IJIST(28), No. 4, December 2018, pp. 295-301.
WWW Link.
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
BibRef
Chen, S.C.[Sheng-Cong],
Ding, C.X.[Chang-Xing],
Liu, M.F.[Min-Feng],
Dual-force convolutional neural networks for accurate brain tumor
segmentation,
PR(88), 2019, pp. 90-100.
Elsevier DOI
1901
Brain tumor segmentation, Dual-force network,
Convolutional neural network, Label distribution, Post-processing
BibRef
Chang, J.[Jie],
Zhang, L.[Luming],
Gu, N.[Naijie],
Zhang, X.[Xiaoci],
Ye, M.[Minquan],
Yin, R.[Rongzhang],
Meng, Q.Q.[Qian-Qian],
A mix-pooling CNN architecture with FCRF for brain tumor segmentation,
JVCIR(58), 2019, pp. 316-322.
Elsevier DOI
1901
MR image segmentation, Convolutional Neural Network, Fully CRF
BibRef
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.
BibRef
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
BibRef
Lipková, J.,
Angelikopoulos, P.,
Wu, S.,
Alberts, E.,
Wiestler, B.,
Diehl, C.,
Preibisch, C.,
Pyka, T.,
Combs, S.E.,
Hadjidoukas, P.,
van Leemput, K.,
Koumoutsakos, P.,
Lowengrub, J.,
Menze, B.,
Personalized Radiotherapy Design for Glioblastoma: Integrating
Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference,
MedImg(38), No. 8, August 2019, pp. 1875-1884.
IEEE DOI
1908
Tumors, Mathematical model, Bayes methods, Biomedical imaging,
Magnetic resonance imaging, Predictive models, Glioblastoma,
multimodal medical scans
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Rajagopal, R.,
Glioma brain tumor detection and segmentation using weighting random
forest classifier with optimized ant colony features,
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.],
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Brain disease diagnosis using local binary pattern and steerable
pyramid,
MultInfoRetr(8), No. 3, September 2019, pp. 155-165.
Springer DOI
1908
BibRef
Gtifa, W.[Wafa],
Hamdaoui, F.[Fayçal],
Sakly, A.[Anis],
3D brain tumor segmentation in MRI images based on a modified PSO
technique,
IJIST(29), No. 4, 2019, pp. 501-509.
DOI Link
1911
2D images, 3D brain tumor segmentation, modified particle swarm optimization
BibRef
Nagarathinam, E.[Ezhilmathi],
Ponnuchamy, T.[Thirumurugan],
Image registration-based brain tumor detection and segmentation using
ANFIS classification approach,
IJIST(29), No. 4, 2019, pp. 510-517.
DOI Link
1911
abnormal cells, classifications, detection, segmentation, tumor
BibRef
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.
DOI Link
1911
abnormal, brain, classification, statistical features, tumors
BibRef
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.
DOI Link
1911
association allotment hierarchical clustering,
gray wolf optimization,
mutual piece-wise linear transformation filtering
BibRef
Meng, H.,
Wang, K.,
Gao, Y.,
Jin, Y.,
Ma, X.,
Tian, J.,
Adaptive Gaussian Weighted Laplace Prior Regularization Enables
Accurate Morphological Reconstruction in Fluorescence Molecular
Tomography,
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
BibRef
Sharif, M.[Muhammad],
Amin, J.[Javaria],
Raza, M.[Mudassar],
Yasmin, M.[Mussarat],
Satapathy, S.C.[Suresh Chandra],
An integrated design of particle swarm optimization (PSO) with fusion
of features for detection of brain tumor,
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Elsevier DOI
2001
BSE, PSO, GA, LBP, Deep features, ANN
BibRef
Amin, J.[Javaria],
Sharif, M.[Muhammad],
Gul, N.[Nadia],
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Shad, S.A.[Shafqat Ali],
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convolutional neural network,
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Elsevier DOI
2001
Sequences, CNN, DWT, Global thresholding, Filter
BibRef
Sharif, M.I.[Muhammad Irfan],
Li, J.P.[Jian Ping],
Khan, M.A.[Muhammad Attique],
Saleem, M.A.[Muhammad Asim],
Active deep neural network features selection for segmentation and
recognition of brain tumors using MRI images,
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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],
Renjit, J.A.[J. Arokia],
Multiclassifier for severity-level categorization of glioma tumors
using multimodal magnetic resonance imaging brain images,
IJIST(30), No. 1, 2020, pp. 234-251.
DOI Link
2002
deep convolutional neural networks,
Jaya optimization algorithm, multimodal MRI brain images,
severity-level classification
BibRef
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],
Lamini, E.S.[El-Sedik],
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
BibRef
Peng, S.[Suting],
Chen, W.[Wei],
Sun, J.W.[Jia-Wei],
Liu, B.[Boqiang],
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
BibRef
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
BibRef
Nasor, M.[Mohamed],
Obaid, W.[Walid],
Detection and localisation of multiple brain tumours by object counting
and elimination,
IET-IPR(14), No. 4, 27 March 2020, pp. 615-620.
DOI Link
2003
BibRef
Goceri, E.[Evgin],
CapsNet topology to classify tumours from brain images and comparative
evaluation,
IET-IPR(14), No. 5, 17 April 2020, pp. 882-889.
DOI Link
2004
BibRef
Tiwari, A.[Arti],
Srivastava, S.[Shilpa],
Pant, M.[Millie],
Brain tumor segmentation and classification from magnetic resonance
images: Review of selected methods from 2014 to 2019,
PRL(131), 2020, pp. 244-260.
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
resnet model,
PRL(133), 2020, pp. 173-179.
Elsevier DOI
2005
Gliomas, Classification, Deep learning, ResNet, Dilated convolution
BibRef
Dandil, E.[Emre],
Biçer, A.[Ali],
Automatic grading of brain tumours using LSTM neural networks on
magnetic resonance spectroscopy signals,
IET-IPR(14), No. 10, August 2020, pp. 1967-1979.
DOI Link
2008
BibRef
Chithra, P.L.,
Dheepa, G.,
Di-phase midway convolution and deconvolution network for brain tumor
segmentation in MRI images,
IJIST(30), No. 3, 2020, pp. 674-686.
DOI Link
2008
brain tumor segmentation,
di-phase midway convolution and deconvolution network,
upsampling
BibRef
Zhang, D.,
Huang, G.,
Zhang, Q.,
Han, J.,
Han, J.,
Wang, Y.,
Yu, Y.,
Exploring Task Structure for Brain Tumor Segmentation From
Multi-Modality MR Images,
IP(29), 2020, pp. 9032-9043.
IEEE DOI
2009
Tumors, Task analysis, Image segmentation,
Brain modeling,
supervised learning
BibRef
Kurmi, Y.[Yashwant],
Chaurasia, V.[Vijayshri],
Classification of magnetic resonance images for brain tumour detection,
IET-IPR(14), No. 12, October 2020, pp. 2808-2818.
DOI Link
2010
BibRef
Mano, A.[Abhisha],
Anand, S.[Swaminathan],
Method of multi-region tumour segmentation in brain MRI images using
grid-based segmentation and weighted bee swarm optimisation,
IET-IPR(14), No. 12, October 2020, pp. 2901-2910.
DOI Link
2010
BibRef
Liu, X.M.[Xiao-Ming],
Zhou, X.B.[Xiao-Bo],
Qian, X.H.[Xiao-Hua],
Transparency-guided ensemble convolutional neural network for the
stratification between pseudoprogression and true progression of
glioblastoma multiform in MRI,
JVCIR(72), 2020, pp. 102880.
Elsevier DOI
2010
Pseudo progression, Glioblastoma multiforme,
Diffusion tensor imaging (DTI), Ensemble CNN
BibRef
Amin, J.[Javeria],
Sharif, M.[Muhammad],
Yasmin, M.[Mussarat],
Fernandes, S.L.[Steven Lawrence],
A distinctive approach in brain tumor detection and classification
using MRI,
PRL(139), 2020, pp. 118-127.
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,
PR(110), 2021, pp. 107562.
Elsevier DOI
2011
Brain tumor segmentation, Cross-modality feature transition,
Cross-modality feature fusion, Feature learning
BibRef
Yepuganti, K.[Karuna],
Saladi, S.[Saritha],
Narasimhulu, C.V.,
Segmentation of tumor using PCA based modified fuzzy C means
algorithms on MR brain images,
IJIST(30), No. 4, 2020, pp. 1337-1345.
DOI Link
2011
brain tumor, DWT, feature extraction, fuzzy C means and MRI
BibRef
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.
DOI Link
2011
brain tumor classification, feature extraction, optimization,
segmentation, skull stripping
BibRef
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,
IJIST(30), No. 4, 2020, pp. 926-938.
DOI Link
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
Capsule Networks,
SPLetters(27), 2020, pp. 2024-2028.
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
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
Li, K.,
Kong, L.,
Zhang, Y.,
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.[Yizhi],
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
Segmentation,
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
BibRef
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
Computer vision, Conferences, 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
BibRef
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],
Åström, F.[Freddie],
Johansson, B.T.[B. Tomas],
Source Localization of Reaction-Diffusion Models for Brain Tumors,
GCPR16(414-425).
Springer DOI
1611
BibRef
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
Martinez-Cortes, T.[Tomas],
Fernandez-Torres, M.A.[Miguel Angel],
Jimenez-Moreno, A.[Amaya],
Gonzalez-Diaz, I.[Ivan],
Diaz-de-Maria, F.[Fernando],
Guzman-De-Villoria, J.A.[Juan Adan],
Fernandez, P.[Pilar],
A Bayesian model for brain tumor classification using clinical-based
features,
ICIP14(2779-2783)
IEEE DOI
1502
Bayes methods
BibRef
Al-Shaikhli, S.D.S.[Saif Dawood Salman],
Yang, M.Y.[Michael Ying],
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