21.9.1 Brain Tumors, Cortex, Cancer

Chapter Contents (Back)
Brain. Brain Tumor. Tumor.
See also Glioma Detection, Analysis, Brain Glioma.
See also Survival Analysis, Cancer Survival.

brain lesion segmentation,
Online
WWW Link. 1605
Code, Brain Lesion Segmentation. multi-scale 3D Deep Convolutional Neural Network coupled with a 3D fully connected Conditional Random Field. BibRef

Kyriacou, S.K., Davatzikos, C., Zinreich, S.J., Bryan, R.N.,
Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI],
MedImg(18), No. 7, July 1999, pp. 580-592.
IEEE Top Reference. 0110
BibRef

Iftekharuddin, K.M.[Khan M.], Jia, W.[Wei], Marsh, R.[Ronald],
Fractal analysis of tumor in brain MR images,
MVA(13), No. 5-6, 2003, pp. 352-362.
WWW Link. 0304

See also FractalNet: A biologically inspired neural network approach to fractal geometry. BibRef

Corso, J.J.[Jason J.], Sharon, E.[Eitan], Dube, S.[Shishir], El-Saden, S.[Suzie], Sinha, U.[Usha], Yuille, A.L.[Alan L.],
Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification,
MedImg(27), No. 5, May 2008, pp. 629-640.
IEEE DOI 0711
BibRef

Al-Kadi, O.S.[Omar S.],
Texture measures combination for improved meningioma classification of histopathological images,
PR(43), No. 6, June 2010, pp. 2043-2053.
Elsevier DOI 1003
BibRef
Earlier:
A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours,
ICIP09(4177-4180).
IEEE DOI 0911
Coloured texture analysis; Feature extraction; Histopathological images; Meningioma; Naive Bayesian classifier; Bhattacharyya distance BibRef

Bourouis, S.[Sami], Hamrouni, K.[Kamel],
3D Segmentation of MRI Brain Using Level Set and Unsupervised Classification,
IJIG(10), No. 1, January 2010, pp. 135-154.
DOI Link 1003
BibRef
Earlier:
An Efficient Framework for Brain Tumor Segmentation in Magnetic Resonance Images,
IPTA08(1-5).
IEEE DOI 0811
BibRef

Bourouis, S.[Sami], Hamrouni, K.[Kamel], Betrouni, N.[Nacim],
Automatic MRI Brain Segmentation with Combined Atlas-Based Classification and Level-Set Approach,
ICIAR08(xx-yy).
Springer DOI 0806

See also New Method for Volume Segmentation of PET Images, Based on Possibility Theory, A. BibRef

Zhang, N.[Nan], Ruan, S.[Su], Lebonvallet, S.[Stephane], Liao, Q.M.[Qing-Min], Zhu, Y.M.[Yue-Min],
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation,
CVIU(115), No. 2, February 2011, pp. 256-269.
Elsevier DOI 1102
BibRef
Earlier:
Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence,
ICIP09(3373-3376).
IEEE DOI 0911
SVM; Segmentation; Feature selection; Fusion; Follow-up system; Brain tumor; MRI BibRef

Boughattas, N.[Naouel], Berar, M.[Maxime], Hamrouni, K.[Kamel], Ruan, S.[Su],
Brain tumor segmentation from multiple MRI sequences using multiple kernel learning,
ICIP14(1887-1891)
IEEE DOI 1502
Feature extraction BibRef

Hamamci, A., Kucuk, N., Karaman, K., Engin, K., Unal, G.,
Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications,
MedImg(31), No. 3, March 2012, pp. 790-804.
IEEE DOI 1203
BibRef

Jayachandran, A., Dhanasekaran, R.,
Automatic detection of brain tumor in magnetic resonance images using multi-texton histogram and support vector machine,
IJIST(23), No. 2, 2013, pp. 97-103.
DOI Link 1307
support vector machine, radial basis function, binarized image BibRef

Jayachandran, A., Dhanasekaran, R.,
Brain tumor severity analysis using modified multi-texton histogram and hybrid kernel SVM,
IJIST(24), No. 1, 2014, pp. 72-82.
DOI Link 1403
tumor BibRef

El-Melegy, M.[Moumen], Mokhtar, H.[Hashim],
Tumor segmentation in brain MRI using a fuzzy approach with class center priors,
JIVP(2014), No. 1, 2014, pp. 21.
DOI Link 1404
BibRef
Earlier:
Incorporating prior information in the fuzzy C-mean algorithm with application to brain tissues segmentation in MRI,
ICIP09(3393-3396).
IEEE DOI 0911
BibRef

Chih-Feng, C.[Chen], Ling-Wei, H.[Hsu], 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 and Ktrans derived from DCE-MRI of brain tumors,
IJIST(22), No. 2, June 2012, pp. 132-136.
DOI Link 1202
BibRef

Angoth, V.[Vivek], Dwith, C.Y.N., Singh, A.[Amarjot],
A Novel Wavelet Based Image Fusion for Brain Tumor Detection,
IJCVSP(2), No. 1, 2013, pp. xx-yy.
WWW Link. 1303
BibRef

Bauer, S.[Stefan], Lu, H.X.[Huan-Xiang], May, C.P.[Christian P.], Nolte, L.P.[Lutz P.], Büchler, P.[Philippe], Reyes, M.[Mauricio],
Integrated segmentation of brain tumor images for radiotherapy and neurosurgery,
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 using combined texture-histogram features,
IJIST(23), No. 2, 2013, pp. 194-203.
DOI Link brain tumor, image processing, association rule mining, associative classifier 1307
BibRef

Arakeri, M.P.[Megha P.], Reddy, G.R.M.[G. Ram Mohana],
An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis,
MultInfoRetr(2), No. 3, September 2013, pp. 175-188.
WWW Link. 1307
BibRef

Arakeri, M.P.[Megha P.], Reddy, G.R.M.[G. Ram Mohana],
Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images,
SIViP(9), No. 2, February 2015, pp. 409-425.
WWW Link. 1503
BibRef

Vilamala, A.[Albert], Lisboa, P.J.G.[Paulo J.G.], Ortega-Martorell, S.[Sandra], Vellido, A.[Alfredo],
Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours,
PRL(34), No. 14, 2013, pp. 1734-1747.
Elsevier DOI 1308
Discriminant Convex Non-negative Matrix Factorization BibRef

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.,
PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration,
MedImg(33), No. 3, March 2014, pp. 651-667.
IEEE DOI 1404
biomedical MRI BibRef

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 using optimization and clustering techniques,
IJIST(24), No. 4, 2014, pp. 313-325.
DOI Link 1411
image segmentation BibRef

Balasubramani, P.[Perumal], Murugan, P.R.[Pallikonda Rajasekaran],
Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach,
IJIST(25), No. 2, 2015, pp. 115-122.
DOI Link 1506
MRI images BibRef

Thapaliya, K.[Kiran], Kwon, G.R.[Goo-Rak],
Identification and extraction of brain tumor from MRI using local statistics of Zernike moments,
IJIST(24), No. 4, 2014, pp. 284-292.
DOI Link 1411
Zernike polynomial BibRef

Kalaiselvi, T., Nagaraja, P.,
A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique,
IJIST(25), No. 1, 2015, pp. 77-85.
DOI Link 1502
brain tumor, clustering, segmentation, thresholding, Fuzzy c-means BibRef

Militello, C.[Carmelo], Rundo, L.[Leonardo], Vitabile, S.[Salvatore], Russo, G.[Giorgio], Pisciotta, P.[Pietro], Marletta, F.[Francesco], Ippolito, M.[Massimo], d'Arrigo, C.[Corrado], Midiri, M.[Massimo], Gilardi, M.C.[Maria Carla],
Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering,
IJIST(25), No. 3, 2015, pp. 213-225.
DOI Link 1509
semi-automatic segmentation BibRef

Stefano, A.[Alessandro], Vitabile, S.[Salvatore], Russo, G.[Giorgio], Ippolito, M.[Massimo], Marletta, F.[Franco], d'Arrigo, C.[Corrado], d'Urso, D.[Davide], Sabini, M.G.[Maria Gabriella], Gambino, O.[Orazio], Pirrone, R.[Roberto], Ardizzone, E.[Edoardo], Gilardi, M.C.[Maria Carla],
An Automatic Method for Metabolic Evaluation of Gamma Knife Treatments,
CIAP15(I:579-589).
Springer DOI 1511
BibRef

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X.T.[Xiao-Tao], Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Raviv, T.R.[T. Riklin], Reza, S.M.S., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.C.[Hoo-Chang], Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H.[Dong Hye], Zhao, L.[Liang], Zhao, B.[Binsheng], Zikic, D., Prastawa, M., Reyes, M., van Leemput, K.,
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),
MedImg(34), No. 10, October 2015, pp. 1993-2024.
IEEE DOI 1511
benchmark testing BibRef

Shanthakumar, P., Kumar, P.G.[P. Ganesh],
Computer aided brain tumor detection system using watershed segmentation techniques,
IJIST(25), No. 4, 2015, pp. 297-301.
DOI Link 1512
enhancement BibRef

Ai, Y.[Ye], Miao, F.[Feng], Hu, Q.M.[Qing-Mao], Li, W.F.[Wei-Feng],
Multi-Feature Guided Brain Tumor Segmentation Based on Magnetic Resonance Images,
IEICE(E98-D), No. 12, December 2015, pp. 2250-2256.
WWW Link. 1601
BibRef

Anitha, V., Murugavalli, S.,
Brain tumour classification using two-tier classifier with adaptive segmentation technique,
IET-CV(10), No. 1, 2016, pp. 9-17.
DOI Link 1601
biomedical MRI BibRef

Jui, S.L., Zhang, S., Xiong, W., Yu, F., Fu, M., Wang, D., Hassanien, A.E.[Aboul Ella], Xiao, K.[Kai],
Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features,
IEEE_Int_Sys(31), No. 2, March 2016, pp. 66-76.
IEEE DOI 1604
Biomedical image processing BibRef

Xiao, K.[Kai], Hassanien, A.E.[Aboul Ella], Sun, Y.[Yan], Ng, E.K.K.[Edwin Kit Keong],
Brain MR Image Tumor Segmentation with Ventricular Deformation,
ICIG11(297-302).
IEEE DOI 1109
BibRef

Stefano, A.[Alessandro], Vitabile, S.[Salvatore], Russo, G.[Giorgio], Ippolito, M.[Massimo], Marletta, F.[Franco], d'Arrigo, C.[Corrado], d'Urso, D.[Davide], Gambino, O.[Orazio], Pirrone, R.[Roberto], Ardizzone, E.[Edoardo], Gilardi, M.C.[Maria Carla],
A fully automatic method for biological target volume segmentation of brain metastases,
IJIST(26), No. 1, 2016, pp. 29-37.
DOI Link 1604
random walk BibRef

Pereira, S., Pinto, A., Alves, V., Silva, C.A.,
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,
MedImg(35), No. 5, May 2016, pp. 1240-1251.
IEEE DOI 1605
Brain modeling BibRef

Thirumurugan, P., Ramkumar, D., Batri, K., Raja, D.S.[D. Sundhara],
Automated detection of glioblastoma tumor in brain magnetic imaging using ANFIS classifier,
IJIST(26), No. 2, 2016, pp. 151-156.
DOI Link 1606
contourlet transform BibRef

Kathirvel, R., Batri, K.,
A computer-aided approach for meningioma brain tumor detection using CANFIS classifier,
IJIST(27), No. 3, 2017, pp. 193-200.
DOI Link 1708
accuracy, brain tumor, classifier, contourlet transform, , features BibRef

Kathirvel, R., Batri, K.,
Detection and diagnosis of meningioma brain tumor using ANFIS classifier,
IJIST(27), No. 3, 2017, pp. 187-192.
DOI Link 1708
brain tumors, detection, diagnosis, features, , tissues BibRef

Thirumurugan, P., Shanthakumar, P.,
Brain tumor detection and diagnosis using ANFIS classifier,
IJIST(26), No. 2, 2016, pp. 157-162.
DOI Link 1606
gray matter, white matter, CSF, brain tumor, brain tissue BibRef

Kumarganesh, S., Suganthi, M.,
An efficient approach for brain image (tissue) compression based on the position of the brain tumor,
IJIST(26), No. 4, 2016, pp. 237-242.
DOI Link 1701
brain tumor BibRef

Thiruvenkadam, K.[Kalaiselvi], Perumal, N.[Nagaraja],
Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique,
IJIST(26), No. 4, 2016, pp. 305-314.
DOI Link 1701
clustering, fuzzy c-means, segmentation, tumor, wavelet BibRef

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 combination of optimization and fuzzy clustering improved by seed-based region growing algorithm,
IJIST(27), No. 1, 2017, pp. 33-45.
DOI Link 1704
MPSO-based FCM BibRef

Angulakshmi, M., Priya, G.G.L.[G.G. Lakshmi],
Automated brain tumour segmentation techniques: A review,
IJIST(27), No. 1, 2017, pp. 66-77.
DOI Link 1704
review BibRef

Farhi, L.[Lubna], Yusuf, A.[Adeel], Raza, R.H.[Rana Hammad],
Adaptive stochastic segmentation via energy-convergence for brain tumor in MR images,
JVCIR(46), No. 1, 2017, pp. 303-311.
Elsevier DOI 1706
Active, contours BibRef

Kaur, T.[Taranjit], Saini, B.S.[Barjinder Singh], Gupta, S.[Savita],
Quantitative metric for MR brain tumour grade classification using sample space density measure of analytic intrinsic mode function representation,
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

Ravě, 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

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

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.J.[Nai-Jie], Zhang, X.C.[Xiao-Ci], Ye, M.Q.[Min-Quan], Yin, R.Z.[Rong-Zhang], 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

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,
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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],
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Amin, J.[Javaria], Sharif, M.[Muhammad], Gul, N.[Nadia], Yasmin, M.[Mussarat], Shad, S.A.[Shafqat Ali],
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Brain tumor, Contrast improvement, Deep saliency method, Features extraction, Optimization, Recognition 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,
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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

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.
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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,
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CapsNet topology to classify tumours from brain images and comparative evaluation,
IET-IPR(14), No. 5, 17 April 2020, pp. 882-889.
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Automatic grading of brain tumours using LSTM neural networks on magnetic resonance spectroscopy signals,
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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.
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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.
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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

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

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

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

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

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],
Retraction: Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm,
IJIST(34), No. 2, 2024, pp. e23038.
DOI Link 2402
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Zhang, W.B.[Wen-Bo], 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

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

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
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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

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

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

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

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

Zhou, T.X.[Tong-Xue],
Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning,
PR(149), 2024, pp. 110282.
Elsevier DOI 2403
Brain tumor segmentation, Multi-modal feature fusion, Disentangled representation learning, Contrastive learning 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

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

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

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.Y.[Tong-Yuan], 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

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

Xue, J.[Jie], Li, Q.[Qi], Liu, X.[Xiyu], Guo, Y.J.[Yu-Jie], Lu, J.[Jie], Song, B.S.[Bo-Sheng], 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

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.C.[Bing-Chao], 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

Gull, S.[Sahar], Akbar, S.[Shahzad], Naqi, S.M.[Syed Muhammad],
A deep learning approach for multi-stage classification of brain tumor through magnetic resonance images,
IJIST(33), No. 5, 2023, pp. 1745-1766.
DOI Link 2310
brain tumor segmentation, convolutional neural network, deep learning, fast bounding box, magnetic resonance imaging, multistage classification BibRef

Shaker, E.A.[Esraa Asem], El-Hossiny, A.S.[Ahmed S.], Kandil, A.H.[Ahmed Hisham], Elbialy, A.[Ahmed], Afify, H.M.[Heba M.],
Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches,
IJIST(33), No. 5, 2023, pp. 1661-1671.
DOI Link 2310
brain tumor images, classification, feature extraction, machine learning, magnetic resonance imaging BibRef

Zia, M.S.[Muhammad Sultan], Baig, U.A.[Usman Ali], Rehman, Z.U.[Zaka Ur], Yaqub, M.[Muhammad], Ahmed, S.[Shahzad], Zhang, Y.D.[Yu-Dong], Wang, S.[Shuihua], Khan, R.[Rizwan],
Contextual information extraction in brain tumour segmentation,
IET-IPR(17), No. 12, 2023, pp. 3371-3391.
DOI Link 2310
attention gate, attentional residual dropout block, context aware 3D ARDUNet, convolutional neural networks, residual dropout block BibRef

Das, P.[Poulomi], Das, A.[Arpita],
Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors,
PR(144), 2023, pp. 109879.
Elsevier DOI 2310
Advanced PCNN module, Classification, FCM clustering algorithm, Interlayer feature quantifiers, NSST based decomposition BibRef

Li, Q.[Qiang], Liu, H.X.[Heng-Xin], Nie, W.Z.[Wei-Zhi], Wu, T.[Ting],
Brain tumor image segmentation based on prior knowledge via transformer,
IJIST(33), No. 6, 2023, pp. 2073-2087.
DOI Link 2311
attention mechanism, brain tumor segmentation, prior knowledge, transformer BibRef

Sultana, T.[Tania], Kurosaki, S.[Sho], Jitsumatsu, Y.[Yutaka], Kuhara, S.[Shigehide], Takeuchi, J.[Jun'ichi],
Brain Tumor Classification using Under-Sampled k-Space Data: A Deep Learning Approach,
IEICE(E106-D), No. 11, November 2023, pp. 1831-1841.
WWW Link. 2311
BibRef

Khan, S.U.R.[Saif Ur Rehman], Zhao, M.[Ming], Asif, S.[Sohaib], Chen, X.[Xuehan],
Hybrid-NET: A fusion of DenseNet169 and advanced machine learning classifiers for enhanced brain tumor diagnosis,
IJIST(34), No. 1, 2024, pp. e22975.
DOI Link 2401
brain tumor, DenseNet169, machine learning classifier, MRI, transfer learning BibRef

Khushi, H.M.T.[Hafiz Muhammad Tayyab], Masood, T.[Tehreem], Jaffar, A.[Arfan], Akram, S.[Sheeraz], Bhatti, S.M.[Sohail Masood],
Performance analysis of state-of-the-art CNN architectures for brain tumour detection,
IJIST(34), No. 1, 2024, pp. e22949.
DOI Link 2401
artificial intelligence, Br35h, brain tumour, deep learning, machine learning, medical image analysis BibRef

Singh, N.H.[Ngangbam Herojit], Merlin, N.R.G.[N. R. Gladiss], Prabu, R.T.[R. Thandaiah], Gupta, D.[Deepak], Alharbi, M.[Meshal],
Multi-classification of brain tumor by using deep convolutional neural network model in magnetic resonance imaging images,
IJIST(34), No. 1, 2024, pp. e22951.
DOI Link 2401
brain tumor, classification, convolutional neural network, deep learning, HPSGWO BibRef

Agrawal, T.[Tarun], Choudhary, P.[Prakash], Shankar, A.[Achyut], Singh, P.[Prabhishek], Diwakar, M.[Manoj],
MultiFeNet: Multi-scale feature scaling in deep neural network for the brain tumour classification in MRI images,
IJIST(34), No. 1, 2024, pp. e22956.
DOI Link 2401
brain tumour detection, deep learning, hybrid pooling, MRI images, multi-scale feature BibRef

Raza, A.[Asif], Alshehri, M.S.[Mohammed S.], Almakdi, S.[Sultan], Siddique, A.A.[Ali Akbar], Alsulami, M.[Mohammad], Alhaisoni, M.[Majed],
Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection,
IJIST(34), No. 1, 2024, pp. e22957.
DOI Link 2401
brain tumor classification, deep learning, DenseNet-121, Inception V3, transfer learning BibRef

Datta, P.[Priyanka], Rohilla, R.[Rajesh],
Brain tumor image pixel segmentation and detection using an aggregation of GAN models with vision transformer,
IJIST(34), No. 1, 2024, pp. e22979.
DOI Link 2401
brain cancer, generative adversarial networks, magnetic resonance imaging, pixel segmentation, vision transformer BibRef

Soni, V.[Vaibhav], Singh, N.K.[Nikhil Kumar], Singh, R.K.[Rishi Kumar], Tomar, D.S.[Deepak Singh],
Multiencoder-based federated intelligent deep learning model for brain tumor segmentation,
IJIST(34), No. 1, 2024, pp. e22981.
DOI Link 2401
artificial intelligent dilated convolution, brain tumor segmentation, channel attention, multi-encoder BibRef

Mehrotra, R.[Rajat], Ansari, M.A., Agrawal, R.[Rajeev], Al-Ward, H.[Hisham], Tripathi, P.[Pragati], Singh, J.[Jay],
An enhanced framework for identifying brain tumor using discrete wavelet transform, deep convolutional network, and feature fusion-based machine learning techniques,
IJIST(34), No. 1, 2024, pp. e22983.
DOI Link 2401
brain tumor, deep convolutional network, DWT, feature fusion, MRI BibRef

Mazher, M.[Moona], Qayyum, A.[Abdul], Puig, D.[Domenec], Abdel-Nasser, M.[Mohamed],
Deep learning-based survival prediction of brain tumor patients using attention-guided 3D convolutional neural network with radiomics approach from multimodality magnetic resonance imaging,
IJIST(34), No. 1, 2024, pp. e23010.
DOI Link 2401
brain tumor, brain tumor prognosis, deep learning, medical image processing, multimodal brain tumor, radiomics, survival prediction BibRef

Ullah, N.[Naeem], Hassan, M.[Muhammad], Khan, J.A.[Javed Ali], Anwar, M.S.[Muhammad Shahid], Aurangzeb, K.[Khursheed],
Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images,
IJIST(34), No. 1, 2024, pp. e23012.
DOI Link 2401
brain-tumor detection, deep learning, explainable AI, LIME, MRI BibRef

Ay, S.[Sevket], Ekinci, E.[Ekin], Garip, Z.[Zeynep],
A brain tumour classification on the magnetic resonance images using convolutional neural network based privacy-preserving federated learning,
IJIST(34), No. 1, 2024, pp. e23018.
DOI Link 2401
classification, deep learning, federated learning, privacy-preserving BibRef

Ramkumar, M., Kumar, R.S.[R. Sarath], Padmapriya, R., Karthick, S.,
Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network,
IJIST(34), No. 1, 2024, pp. e23005.
DOI Link 2401
brain tumor segmentation, BraTS, magnetic resonance imaging, overall survival prediction, radiomics features BibRef

Su, J.[Jianpo], Shen, H.[Hui], Peng, L.M.[Li-Min], Hu, D.[Dewen],
Few-Shot Domain-Adaptive Anomaly Detection for Cross-Site Brain Images,
PAMI(46), No. 3, March 2024, pp. 1819-1835.
IEEE DOI 2402
Anomaly detection, Mental disorders, Functional magnetic resonance imaging, Training, fMRI functional connectivity BibRef

Liu, H.[Huabing], Ni, Z.Z.[Zheng-Ze], Nie, D.[Dong], Shen, D.G.[Ding-Gang], Wang, J.[Jinda], Tang, Z.Y.[Zhen-Yu],
Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images,
IP(33), 2024, pp. 1199-1210.
IEEE DOI Code:
WWW Link. 2402
Tumors, Brain, Image segmentation, Convolution, Correlation, Lesions, Image reconstruction, Brain tumor segmentation, BraTS2022 dataset BibRef

Kumar, S.[Sangeet], Biswal, B.,
MAEU-NET: A novel supervised architecture for brain tumor segmentation,
IJIST(34), No. 2, 2024, pp. e22988.
DOI Link 2402
brain tumor, context aggregation, MAEU-net, parallel pooling module, receptive field strength, segmentation BibRef

Bhuyan, R.[Ranadeep], Nandi, G.[Gypsy],
Systematic study and design of multimodal MRI image augmentation for brain tumor detection with loss aware exchange and residual networks,
IJIST(34), No. 2, 2024, pp. e22989.
DOI Link 2402
brain tumor, data augmentation, deep learning, magnetic resonance imaging (MRI), residual learning, UNet BibRef

Aggarwal, M.[Meenakshi], Khullar, V.[Vikas], Goyal, N.[Nitin], Rastogi, R.[Rashi], Singh, A.[Aman], Torres, V.Y.[Vanessa Yelamos], Albahar, M.A.[Marwan Ali],
Privacy preserved collaborative transfer learning model with heterogeneous distributed data for brain tumor classification,
IJIST(34), No. 2, 2024, pp. e22994.
DOI Link 2402
brain tumor, convolutional neural network, deep learning, federated learning, independent and identically distributed, transfer learning BibRef

Mehmood, Y.[Yasar], Bajwa, U.I.[Usama Ijaz], Anwar, M.W.[Muhammad Waqas],
Brain tumor grade classification using multi-step pre-training,
IJIST(34), No. 2, 2024, pp. e23008.
DOI Link 2402
brain tumor, computational efficiency, domain adaptive pre-training, transfer learning BibRef

Dheepak, G., Christaline, J.A.[J. Anita], Vaishali, D.,
MEHW-SVM multi-kernel approach for improved brain tumour classification,
IET-IPR(18), No. 4, 2024, pp. 856-874.
DOI Link 2403
brain tumours, global grey level co-occurrence matrix (GLCM), local binary patterns (LBP), principal component analysis (PCA) BibRef

Vatanpour, M.[Marjan], Haddadnia, J.[Javad],
Brain tumour segmentation of MR images based on custom attention mechanism with transfer-learning,
IET-IPR(18), No. 4, 2024, pp. 886-896.
DOI Link 2403
image segmentation, medical image processing BibRef

Singha, A.[Anu], Venkateswaran, V.[Vanitha],
A Study on Effective Segmentation Network in MRI Images for Diagnosis of Brain Tumor,
ICCVMI23(1-7)
IEEE DOI 2403
Shape, Magnetic resonance imaging, Semantic segmentation, Computational modeling, skip connections BibRef

Chen, R.[Runlin], Lin, Y.[Yangping], Ren, Y.M.[Yan-Ming], Deng, H.[Hao], Cui, W.[Wenyao], Liu, W.J.[Wen-Jie],
An efficient brain tumor segmentation model based on group normalization and 3D U-Net,
IJIST(34), No. 3, 2024, pp. e23072.
DOI Link 2404
attention, brain tumor, efficient, group normalization, segmentation BibRef

Ghahramani, M.[Marzieh], Shiri, N.[Nabiollah],
An adaptive neuro-fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images,
IET-IPR(18), No. 5, 2024, pp. 1358-1372.
DOI Link 2404
biomedical MRI, neural net architecture, tumours BibRef

Wang, Z.K.[Ze-Kun], Zou, Y.[Yanni], Chen, H.Y.[Hong-Yu], Liu, P.X.[Peter X.], Chen, J.Y.[Jun-Yu],
Multi-scale features and attention guided for brain tumor segmentation,
JVCIR(100), 2024, pp. 104141.
Elsevier DOI 2405
Brain tumor segmentation, Convolutional neural network, Magnetic resonance images, Dilated convolution BibRef

Xu, Y.[Yang], Yu, K.[Kun], Qi, G.Q.[Guan-Qiu], Gong, Y.F.[Yi-Fei], Qu, X.L.[Xiao-Long], Yin, L.[Li], Yang, P.[Pan],
Brain tumour segmentation framework with deep nuanced reasoning and Swin-T,
IET-IPR(18), No. 6, 2024, pp. 1550-1564.
DOI Link Code:
WWW Link. 2405
image segmentation, medical image processing BibRef

Zhu, Z.Q.[Zhi-Qin], Wang, Z.[Ziyu], Qi, G.Q.[Guan-Qiu], Mazur, N.[Neal], Yang, P.[Pan], Liu, Y.[Yu],
Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction,
PR(153), 2024, pp. 110553.
Elsevier DOI 2405
Brain tumor segmentation, Multi-modality MRI, Spatial information enhancement, Boundary shape correction BibRef

Hu, Y.J.[Yuan-Jing], Huang, A.[Aibin], Xu, R.[Rui],
HAB-Net: Hierarchical asymmetric convolution and boundary enhancement network for brain tumor segmentation,
IET-IPR(18), No. 7, 2024, pp. 1809-1822.
DOI Link 2405
Brain tumor segmentation, Boundary attention, hierarchical convolution, magnetic resonance images, Transformer BibRef

Wen, J.[Jinyu], Khan, A.[Asad], Chen, A.[Amei], Peng, W.L.[Wei-Long], Fang, M.[Meie], Chen, C.L.P.[C. L. Philip], Li, P.[Ping],
High-Quality Fusion and Visualization for MR-PET Brain Tumor Images via Multi-Dimensional Features,
IP(33), 2024, pp. 3550-3563.
IEEE DOI Code:
WWW Link. 2406
Feature extraction, Image fusion, Wavelet transforms, Image color analysis, Deep learning, Visualization, affine transformation BibRef

Zhou, L.F.[Li-Fang], Jiang, Y.[Yu], Li, W.S.[Wei-Sheng], Hu, J.[Jun], Zheng, S.[Shenhai],
Shape-Scale Co-Awareness Network for 3D Brain Tumor Segmentation,
MedImg(43), No. 7, July 2024, pp. 2495-2508.
IEEE DOI Code:
WWW Link. 2407
Tumors, Shape, Image segmentation, Brain modeling, Transformers, Convolution, Brain tumor segmentation, shape-aware, transformer, MLP, multi-scale BibRef


Konwer, A.[Aishik], Hu, X.L.[Xiao-Ling], Bae, J.[Joseph], Xu, X.[Xuan], Chen, C.[Chao], Prasanna, P.[Prateek],
Enhancing Modality-Agnostic Representations via Meta-learning for Brain Tumor Segmentation,
ICCV23(21358-21368)
IEEE DOI 2401
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Qiu, Y.S.[Yan-Sheng], Chen, D.[Delin], Yao, H.[Hongdou], Xu, Y.C.[Yong-Chao], Wang, Z.[Zheng],
Scratch Each Other's Back: Incomplete Multi-modal Brain Tumor Segmentation Via Category Aware Group Self-Support Learning,
ICCV23(21260-21269)
IEEE DOI Code:
WWW Link. 2401
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Imran, M.[Muhammad], Qureshi, H.K.[Hassaan Khaliq], Amerini, I.[Irene],
BHAC-MRI: Backdoor and Hybrid Attacks on MRI Brain Tumor Classification Using CNN,
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AN L2-Normalized Spatial Attention Network for Accurate and Fast Classification of Brain Tumors in 2D T1-Weighted CE-MRI Images,
ICIP23(1895-1899)
IEEE DOI Code:
WWW Link. 2312
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Do, N.T.[Nhu-Tai], Vo-Thanh, H.S.[Hoang-Son], Nguyen-Quynh, T.T.[Tram-Tran], Kim, S.H.[Soo-Hyung],
3D-DDA: 3D Dual-Domain Attention for Brain Tumor Segmentation,
ICIP23(3215-3219)
IEEE DOI 2312
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Chen, Y.T.[Yi Tang], Kurtek, S.[Sebastian],
Shape and Intensity Analysis of Glioblastoma Multiforme Tumors,
TAG-PRA23(553-560)
IEEE DOI 2309
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Wang, Y.[Yu], Ji, Y.R.[Ya-Rong],
TensorMixup Data Augmentation Method for Fully Automatic Brain Tumor Segmentation,
ICPR22(4615-4622)
IEEE DOI 2212
Training, Image segmentation, Tensors, Magnetic resonance imaging, Data models, Task analysis, TensorMixup, Magnetic resonance imaging 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

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

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

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],
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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,
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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

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,
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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.],
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Springer DOI 1909
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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

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

Urien, H.[Hélčne], Buvat, I.[Irčne], 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., Madrońal, 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], Ĺström, 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

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], 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

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.M.[William M.], 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
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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
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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
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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
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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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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.H.[Jong-Hyun], Park, S.[Soonyoung], Kim, S.Y.[Sooh-Yung], Kim, S.[Sunworl], Ahn, G.[Gukdong], Lee, M.[Myungeun], Lee, G.S.[Guee-Sang],
Level-Set Segmentation of Brain Tumors Using a New Hybrid Speed Function,
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
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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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Verma, N.K., Gupta, P., Agrawal, P., Cui, Y.[Yan],
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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Rendering of brain tumors using endoneurosonography,
ICPR08(1-4).
IEEE DOI 0812
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Machucho-Cadena, R.[Ruben], de la Cruz-Rodríguez, S.[Sergio], Bayro-Corrochano, E.[Eduardo],
Joint Freehand Ultrasound and Endoscopic Reconstruction of Brain Tumors,
CIARP08(691-698).
Springer DOI 0809
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Machucho-Cadena, R.[Ruben], Moya-Sánchez, E.[Eduardo], de la Cruz-Rodríguez, S.[Sergio], Bayro-Corrochano, E.[Eduardo],
Use of Ultrasound and Computer Vision for 3D Reconstruction,
CIARP09(782-789).
Springer DOI 0911
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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],
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines,
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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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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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Glioma Detection, Analysis, Brain Glioma .


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