References
- [1] S. B. Akers, Binary decision diagrams, IEEE Trans. ComputersC-27, 6 (1978) 509–516. ⇒ 11510.1109/TC.1978.1675141
- [2] A. J. Asman, B. A. Landman, Out-of-atlas labeling: a multi-atlas approach to cancer segmentation, Proc. IEEE International Symposium on Biomedical Imaging, Barcelona, Catalunya, 2012, pp. 1236–1239. ⇒ 11110.1109/ISBI.2012.6235785
- [3] L. Breiman, Random forests, Machine Learning45, 1 (2001) 5–32. ⇒ 11710.1023/A:1010933404324
- [4] J. D. Christensen, Normalization of brain magnetic resonance images using histogram even-order derivative analysis, Magn. Reson. Imaging21, 7 (2003) 817–820. ⇒ 11410.1016/S0730-725X(03)00102-4
- [5] S. Ghanavati, J. Li, T. Liu, P. S. Babyn, W. Doda, G. Lampropoulos, Automatic brain tumor detection in magnetic resonance images, Proc. IEEE International Symposium on Biomedical Imaging, Barcelona, Catalunya, 2012, pp. 574–577. ⇒ 11110.1109/ISBI.2012.6235613
- [6] N. Gordillo, E. Montseny, P. Sobrevilla, State of the art survey on MRI brain tumor segmentation, Magn. Reson. Imaging31 (2013) 1426–1438. ⇒ 111, 11210.1016/j.mri.2013.05.00223790354
- [7] A. Hamamci, N. Kucuk, K. Karamam, K. Engin, G. Unal, Tumor-Cut: segmentation of brain tumors on contranst enhanced MR images for radiosurgery applications, IEEE Trans. Med. Imaging31 (2012) 790–804. ⇒ 11110.1109/TMI.2011.2181857
- [8] M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P. M. Jodoin, H. Larochelle, Brain tumor segmentation with deep neural networks, Med. Image Anal. 35 (2017) 18–31. ⇒ 11210.1016/j.media.2016.05.00427310171
- [9] M. Y. Huang, W. Yang, Y. Wu, J. Jiang, W. F. Chen, Q. J. Feng, Brain tumor segmentation based on local independent projection-based classification, IEEE Trans. Biomed. Eng. 61 (2014) 2633–2645. ⇒ 11210.1109/TBME.2014.232541024860022
- [10] J. E. Iglesias, M. R. Sabuncu, Multi-atlas segmentation of biomedical images: A survey, Med. Image Anal. 24 (2015) 205–219. ⇒ 11210.1016/j.media.2015.06.012453264026201875
- [11] A. Islam, S. M. S. Reza, K. M. Iftekharuddin, Multifractal texture estimation for detection and segmentation of brain tumors, IEEE Trans. Biomed. Eng. 60 (2013) 3204–3215. ⇒ 11210.1109/TBME.2013.2271383512698023807424
- [12] J. Juan-Albarracín, E. Fuster-Garcia, J. V. Manjón, M. Robles, F. Aparici, L. Martí-Bonmatí, J. M. García-Gómez, Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification, PLoS ONE10 5 (2015) e0125143. ⇒ 11210.1371/journal.pone.0125143
- [13] V. G. Kanas, E. I. Zacharaki, C. Davatzikos, K. N. Sgarbas, V. Megalooikonomou, A low cost approach for brain tumor segmentation based on intensity modeling and 3D random walker, Biomed. Sign. Proc. Control22 (2015) 19–30. ⇒ 11210.1016/j.bspc.2015.06.004
- [14] Z. Kapás, L. Lefkovits, D. Iclănzan, Á. Győrfi, B. L. Iantovics, Sz. Lefkovits, S. M.. Szilágyi, L. Szilágyi, Automatic brain tumor segmentation in multispectral MRI volumes using a random forest approach, Proc. Pacific-Rim Symposium on Image and Video Technology (PSIVT’17), Lecture Notes in Artificial Intelligence10749 (2018) 137–149. ⇒ 11210.1007/978-3-319-75786-5_12
- [15] M. Lê, H. Delingette, J. Kalpathy-Cramer, E. R. Gerstner, T. Batchelor, J. Unkelbach, N. Ayache, Personalized radiotherapy planning based on a computational tumor growth model, IEEE Trans. Med. Imaging36 (2017) 815–825. ⇒ 11210.1109/TMI.2016.262644328113925
- [16] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imaging34, 10 (2015) 1993–2024. ⇒ 114, 11810.1109/TMI.2014.2377694483312225494501
- [17] B. H. Menze, K. van Leemput, D. Lashkari, T. Riklin-Raviv, E. Geremia, E. Alberts, et al., A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke, IEEE Trans. Med. Imaging35 (2016) 933–946. ⇒ 11210.1109/TMI.2015.2502596485496126599702
- [18] I. Njeh, L. Sallemi, I. Ben Ayed, K. Chtourou, S. Lehericy, D. Galanaud, A. Ben Hamida, 3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach, Comput. Med. Image Anal. 40 (2015) 108–119. ⇒ 11110.1016/j.compmedimag.2014.10.00925467804
- [19] L. G. Nyúl, J. K. Udupa, X. Zhang, New variants of a method of MRI scale standardization, IEEE Trans. Med. Imaging19, 2 (2010) 143–150. ⇒ 111, 11410.1109/42.83637310784285
- [20] S. Pereira, A. Pinto, V. Alves, C. A. Silva, Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Med. Imaging35 (2016) 1240–1251. ⇒ 11210.1109/TMI.2016.253846526960222
- [21] A. Pinto, S. Pereira, D. Rasteiro, C. A. Silva, Hierarchical brain tumour segmentation using extremely randomized trees, Patt. Recogn. 82 (2018) 105–117. ⇒ 112, 12910.1016/j.patcog.2018.05.006
- [22] J. Sahdeva, V. Kumar, I. Gupta, N. Khandelwal, C. K. Ahuja, A novel content-based active countour model for brain tumor segmentation, Magn. Reson. Imaging30 (2012) 694–715. ⇒ 11110.1016/j.mri.2012.01.00622459443
- [23] H. C. Shin, H. R. Roth, M. C. Gao, L. Lu, Z. Y. Xu, I. Nogues, J. H. Yao, D. Mollura, R. M. Summers, Deep nonvolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Med. Imaging35 (2016) 1285–1298. ⇒ 11210.1109/TMI.2016.2528162
- [24] Zs. Szabó, Z. Kapás, Á. Győrfi, L. Lefkovits, S. M. Szilágyi, L. Szilágyi, Automatic segmentation of low-grade brain tumor using a random forest classifier and Gabor features, Proc. 14th International Conference on Fuzzy Systems and Knowledge Discovery, Huangshan, China, 2018, pp. 1106–1113. ⇒ 112
- [25] L. Szilágyi, L. Lefkovits, B. Benyó, Automatic Brain Tumor Segmentation in multispectral MRI volumes using a fuzzy c-means cascade algorithm, Proc. 11th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, 2015, pp. 285–291. ⇒ 11210.1109/FSKD.2015.7381955
- [26] N. J. Tustison, K. L. Shrinidhi, M. Wintermark, C. R. Durst, B. M. Kandel, J. C. Gee, M. C. Grossman, B. B. Avants, Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR, Neuroinformatics13 (2015) 209–225. ⇒ 11210.1007/s12021-014-9245-225433513
- [27] U. Vovk, F. Pernus, B. Likar, A review of methods for correction of intensity inhomogeneity in MRI, IEEE Trans. Med. Imaging26 (2007) 405–421. ⇒ 11110.1109/TMI.2006.89148617354645
- [28] R. Zaouche, A. Belaid, S. Aloui, B. Solaiman, L. Lecornu, D. Ben Salem, S. Tliba, Semi-automatic method for low-grade gliomas segmentation in magnetic resonance imaging, IRBM39 (2018) 116–128. ⇒ 11210.1016/j.irbm.2018.01.004
- [29] N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao, Y. Zhou, Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation, Comput. Vis. Image Undestand. 115 (2011) 256–269. ⇒ 11210.1016/j.cviu.2010.09.007