20.9.1 Brain Tumors, Cortex, Cancer

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

Zhou, T., Canu, S., Vera, P., Ruan, S.,
Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities,
IP(30), 2021, pp. 4263-4274.
IEEE DOI 2104
Tumors, Image segmentation, Correlation, Magnetic resonance imaging, Brain modeling, Feature extraction, deep learning 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], Agahi, H.[Hamed], Mahmoodzadeh, A.[Azar],
Multi-objective differential evolution-based ensemble method for brain tumour diagnosis,
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 BibRef

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.
DOI Link 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 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,
PRL(129), 2020, pp. 150-157.
Elsevier DOI 2001
BSE, PSO, GA, LBP, Deep features, ANN BibRef

Amin, J.[Javaria], Sharif, M.[Muhammad], Gul, N.[Nadia], Yasmin, M.[Mussarat], Shad, S.A.[Shafqat Ali],
Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network,
PRL(129), 2020, pp. 115-122.
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,
PRL(129), 2020, pp. 181-189.
Elsevier DOI 2001
Brain tumor, Contrast improvement, Deep saliency method, Features extraction, Optimization, Recognition BibRef

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 BibRef

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

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

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

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], Rosenhahn, B.[Bodo],
Brain tumor classification using sparse coding and dictionary learning,
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], Jodoin, P.M.[Pierre-Marc], Larochelle, H.[Hugo],
Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification,
ICPR14(556-561)
IEEE DOI 1412
Brain BibRef

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 BibRef

Subbanna, N.[Nagesh], Precup, D.[Doina], Arbel, T.[Tal],
Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI,
CVPR14(400-405)
IEEE DOI 1409
BibRef

Drakopoulos, F.[Fotis], Chrisochoides, N.P.[Nikos P.],
A Parallel Adaptive Physics-Based Non-rigid Registration Framework for Brain Tumor Resection,
CompIMAGE14(57-68).
Springer DOI 1407
BibRef

Nasir, M.[Muhammad], Baig, A.[Asim], Khanum, A.[Aasia],
Brain Tumor Classification in MRI Scans Using Sparse Representation,
ICISP14(629-637).
Springer DOI 1406
BibRef

Kvet, M.[Michal], Kvet, M.[Marek], Matiasko, K.[Karol],
Application for brain tumour imaging,
WSSIP14(47-50) 1406
Atmospheric measurements BibRef

Parisot, S.[Sarah], Wells, W.[William], Chemouny, S.[Stephane], Duffau, H.[Hugues], Paragios, N.[Nikos],
Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration,
ICCV13(641-648)
IEEE DOI 1403
BibRef

Parisot, S.[Sarah], Duffau, H.[Hugues], Chemouny, S.[Stephane], Paragios, N.[Nikos],
Graph-based detection, segmentation and characterization of brain tumors,
CVPR12(988-995).
IEEE DOI 1208
BibRef

Salah, M.B.[Mohamed Ben], Diaz, I.[Idanis], Greiner, R.[Russell], Boulanger, P.[Pierre], Hoehn, B.[Bret],
Fully Automated Brain Tumor Segmentation Using Two MRI Modalities,
ISVC13(I:30-39).
Springer DOI 1310
BibRef

Bauer, S.[Stefan], Tessier, J.[Jean], Krieter, O.[Oliver], Nolte, L.P.[Lutz P.], Reyes, M.[Mauricio],
Integrated Spatio-Temporal Segmentation of Longitudinal Brain Tumor Imaging Studies,
MCV13(74-83).
Springer DOI 1405
BibRef

Sridhar, D., Krishna, I.M.[IV. Murali],
Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network,
ICSIPR13(92-96).
IEEE DOI 1304
BibRef

Geremia, E.[Ezequiel], Menze, B.H.[Bjoern H.], Prastawa, M.[Marcel], Weber, M.A., Criminisi, A.[Antonio],
Brain Tumor Cell Density Estimation from Multi-modal MR Images Based on a Synthetic Tumor Growth Model,
MCVM12(273-282).
Springer DOI 1305
BibRef

Wei, Z.W.[Zhen-Wen], Zhang, C.M.[Cai-Ming], Yang, X.Q.[Xing-Qiang], Zhang, X.F.[Xiao-Feng],
Segmentation of Brain Tumors in CT Images Using Level Sets,
ISVC12(I: 22-31).
Springer DOI 1209
BibRef

Gasmi, K.[Karim], Kharrat, A.[Ahmed], Messaoud, M.B.[Mohamed Ben], Abid, M.[Mohamed],
Automated Segmentation of Brain Tumor Using Optimal Texture Features and Support Vector Machine Classifier,
ICIAR12(II: 230-239).
Springer DOI 1206
BibRef

Fazlollahi, A., Dowson, N., Meriaudeau, F., Rose, S., Fay, M., Thomas, P., Taylor, Z., Gal, Y., Coultard, A., Winter, C., MacFarlane, D., Salvado, O., Crozier, S., Bourgeat, P.,
Automatic Brain Tumour Segmentation in 18F-FDOPA PET Using PET/MRI Fusion,
DICTA11(325-329).
IEEE DOI 1205
BibRef

Tran, L.[Loc], Banerjee, D.[Deb], Sun, X.Y.[Xiao-Yan], Wang, J.H.[Ji-Hong], Kumar, A.J.[Ashok J.], Vinning, D.[David], McKenzie, F.D.[Frederic D.], Li, Y.H.[Yao-Hang], Li, J.[Jiang],
A Large-Scale Manifold Learning Approach for Brain Tumor Progression Prediction,
MLMI11(265-272).
Springer DOI 1109
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Zoghbi, J.M., Mamede, M.H., Jackowski, M.P.,
Computer-assisted segmentation of brain tumor lesions from multi-sequence Magnetic Resonance Imaging using the Mumford-Shah model,
IVCNZ10(1-6).
IEEE DOI 1203
BibRef

Li, H.M.[Hong-Ming], Song, M.[Ming], Fan, Y.[Yong],
Segmentation of Brain Tumors in Multi-parametric MR Images via Robust Statistic Information Propagation,
ACCV10(IV: 606-617).
Springer DOI 1011
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Wang, T.[Tao], Cheng, I.[Irene], Basu, A.[Anup],
Fully automatic brain tumor segmentation using a normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow,
ICIP10(2553-2556).
IEEE DOI 1009
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Cho, W.[Wanhyun], Park, J.[Jonghyun], Park, S.[Soonyoung], Kim, S.Y.[Sooh-Yung], Kim, S.[Sunworl], Ahn, G.[Gukdong], Lee, M.[Myungeun], Lee, G.S.[Guee-Sang],
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ICPR10(1545-1548).
IEEE DOI 1008
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Gooya, A.[Ali], Biros, G.[George], Davatzikos, C.[Christos],
An EM algorithm for brain tumor image registration: A tumor growth modeling based approach,
MMBIA10(39-46).
IEEE DOI 1006
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Chen, V.[Victor], Ruan, S.[Su],
Graph cut segmentation technique for MRI brain tumor extraction,
IPTA10(284-287).
IEEE DOI 1007
BibRef

Khandani, M.K.[Masoumeh Kalantari], Bajcsy, R.[Ruzena], Fallah, Y.P.[Yaser P.],
Automated Segmentation of Brain Tumors in MRI Using Force Data Clustering Algorithm,
ISVC09(I: 317-326).
Springer DOI 0911
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MRI brain image segmentation for spotting tumors using improved mountain clustering approach,
AIPR09(1-8).
IEEE DOI 0910
BibRef

Avola, D.[Danilo], Cinque, L.[Luigi],
Encephalic NMR Tumor Diversification by Textural Interpretation,
CIAP09(394-403).
Springer DOI 0909
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ICPR08(1-4).
IEEE DOI 0812
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Song, Y.Q.[Yang-Qiu], Zhang, C.S.[Chang-Shui], Lee, J.G.[Jian-Guo], Wang, F.[Fei],
A Discriminative Method For Semi-Automated Tumorous Tissues Segmentation of MR Brain Images,
MMBIA06(79).
IEEE DOI 0609
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Saxena, V., Nielsen, J.F., Gonzalez-Gomez, I., Karapetyan, G., Khankaldyyan, V., Nelson, M.D., Laug, W.E.,
A noninvasive, multimodality approach based on MRS and MRI techniques for monitoring intracranial brain tumor angiogenesis,
AIPR05(127-132).
IEEE DOI 0510
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Lee, C.H.[Chi-Hoon], Schmidt, M.[Mark], Murtha, A.[Albert], Bistritz, A.[Aalo], Sander, J.[Jöerg], Greiner, R.[Russell],
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CVBIA05(469-478).
Springer DOI 0601
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Dam, E., Loog, M., Letteboer, M.,
Integrating automatic and interactive brain tumor segmentation,
ICPR04(III: 790-793).
IEEE DOI 0409
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Leung, C.C., Chen, W.F., Kwok, P.C.K., Chan, F.H.Y.,
Brain tumor boundary detection in MR image with generalized fuzzy operator,
ICIP03(II: 1057-1060).
IEEE DOI 0312
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Capelle, A.S., Colot, O., Fernandez-Maloigne, C.,
Segmentation of multi-modality MR images by means of evidence theory for 3d reconstruction of brain tumors,
ICIP02(II: 773-776).
IEEE DOI 0210
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Capelle, A.S., Alata, O., Fernandez-Maloigne, C., Ferrie, J.,
Unsupervised Algorithm for the Segmentation of Three-dimensional Magnetic Resonance Brain Images,
ICIP01(III: 1047-1050).
IEEE DOI 0108
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Capelle, A.S., Alata, O., Fernandez-Maloigne, C., Lefevre, S.,
Unsupervised Segmentation for Automatic Detection of Brain Tumors in MRI,
ICIP00(Vol I: 613-616).
IEEE DOI 0008
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Ho, S.[Sean], Bullitt, E., Gerig, G.,
Level-set evolution with region competition: Automatic 3-D segmentation of brain tumors,
ICPR02(I: 532-535).
IEEE DOI 0211
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Lorenzen, P., Joshi, S., Gerig, G., Bullitt, E.,
Tumor-Induced Structural and Radiometric Asymmetry in Brain Images,
MMBIA01(xx-yy). 0110
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Mohamed, A., Kyriacou, S.K., Davatzikos, C.[Christos],
A Statistical Approach for Estimating Brain Tumor-Induced Deformation,
MMBIA01(xx-yy). 0110
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