M.E. Davis, "Glioblastoma: Overview of disease and treatment", Clin. J. Oncol. Nurs., vol. 20, p. S2, 2016.
M. Arikan, B. Fröhler, and T. Möller, "Semi-automatic brain tumor segmentation using support vector machines and interactive seed selection"Proc. MICCAI-BRATS Workshop, 2016 , pp. 1-3. 2016
E.A. Eisenhauer, P. Therasse, J. Bogaerts, L.H. Schwartz, D. Sargent, R. Ford, J. Dancey, S. Arbuck, S. Gwyther, M. Mooney, and L. Rubinstein, "New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)", Eur. J. Cancer, vol. 45, pp. 228-247, January 2009.
P.Y. Wen, D.R. Macdonald, D.A. Reardon, T.F. Cloughesy, A.G. Sorensen, E. Galanis, J. DeGroot, W. Wick, M.R. Gilbert, A.B. Lassman, and C. Tsien, "Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group", J. Clin. Oncol., vol. 28, pp. 1963-1972, April 2010.
J. Larsen, S.B. Wharton, F. McKevitt, C. Romanowski, C. Bridgewater, H. Zaki, and N. Hoggard, "‘Low grade glioma’: an update for radiologists", Br. J. Radiol., vol. 90, p. 20160600, February 2017.
J.J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, and A. Yuille, "Efficient multilevel brain tumor segmentation with integrated bayesian model classification", IEEE Trans. Med. Imaging, vol. 27, pp. 629-640, 2008.
B.N. Saha, N. Ray, R. Greiner, A. Murtha, and H. Zhang, "Quick detection of brain tumors and edemas: A bounding box method using symmetry", Comput. Med. Imaging Graph., vol. 36, pp. 95-107, 2012.
J. Dogra, M. Sood, S. Jain, and N. Parashar, "Segmentation of magnetic resonance images of brain using thresholding techniques", 4th IEEE International Conference on Signal Processing, Computing and Control (ISPCC), 2017
J. Dogra, "Improved methods for analyzing MRI brain images", Net. Biol., vol. 8, no. 1, 2018.
Z.M. Wang, Y.C. Soh, Q. Song, and K. Sim, "Adaptive spatial informationtheoretic clustering for image segmentation", Pattern Recognit., vol. 42, no. 9, pp. 2029-2044, 2009.
S.N. Kumar, A. Lenin Fred, and P. Sebastin Varghese, "Suspicious lesion segmentation on brain, mammograms and breast MR images using new optimized spatial feature based super-pixel fuzzy C-means clustering", J. Digit. Imaging, pp. 1-14, 2018.
N.R. Pal, K. Pal, J.M. Keller, and J.C. Bezdek, "A possibilistic fuzzy C-meansclustering algorithm", IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 517-530, 2005.
W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clusteringalgorithms incorporating local information for image segmentation", Pattern Recognit., vol. 40, no. 3, pp. 825-838, 2007.
S. Krinidis, and V. Chatzis, "A robust fuzzy local information c-means clustering algorithm", IEEE Trans. Image Process., vol. 19, no. Issue. 5, pp. 1328-1337, 2010.
"H, Le Capitaine, and C. Frelicot, A cluster-validity index combining anoverlap measure and a separation measure based on fuzzyaggregation operators", IEEE Trans. Fuzzy Syst., vol. 19, no. 3, pp. 580-588, 2011.
A.F. Frangi, D. Rueckert, J.A. Schnabel, and W.J. Niessen, "Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling", IEEE Trans. Med. Imaging, vol. 21, pp. 1151-1166, 2002.
H. Ling, S.K. Zhou, Y. Zheng, B. Georgescu, M. Suehling, and D. Comaniciu, "Hierarchical, learning-based automatic liver segmentation", Computer Vision and Pattern Recognition (CVPR) IEEE
Conference, 2008 pp. 1-8
S. Seifert, A. Barbu, S.K. Zhou, D. Liu, J. Feulner, M. Huber, M. Suehling, A. Cavallaro, and D. Comaniciu, "Hierarchical parsing and semantic navigation of full body CT data", Med. Imaging
2009: Image Processing, 2009 p. 725902
J.A. Sethian, "Level set methods and fast marching methods", J. Comput. Info. Technol., vol. 11, pp. 1-2, 2003.
S.O.R. Fedkiw, and S. Osher, "Level set methods and dynamic implicit surfaces", Surfaces, vol. 44, p. 77, 2002.
J. Yang, and J.S. Duncan, "3D image segmentation of deformable objects with joint shape-intensity prior models using level sets", Med. Image Anal., vol. 8, pp. 285-294, 2004.
R. Malladi, J.A. Sethian, and B.C. Vemuri, "Shape modeling with front propagation: A level set approach", IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, pp. 158-175, 1995.
D. Cremers, S.J. Osher, and S. Soatto, "Kernel density estimation and intrinsic alignment for shape priors in level set segmentation", Int. J. Comput. Vis., vol. 69, pp. 335-351, 2006.
S. Pereira, A. Pinto, V. Alves, and C.A. Silva, "Brain tumor segmentation using convolutional neural networks in MRI images", IEEE Trans. Med. Imaging, vol. 35, pp. 1240-1251, 2016.
P. Liskowski, and K. Krawiec, "Segmenting retinal blood vessels with deep neural networks", IEEE Trans. Med. Imaging, vol. 35, pp. 2369-2380, 2016.
S.P.K. Karri, D. Chakraborty, and J. Chatterjee, "Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration", Biomed. Opt. Express, vol. 8, pp. 579-592, 2017.
Y. Boykov, and M-P. Jolly, "Interactive organ segmentation using graph cuts", International conference on medical image computing
and computer-assisted intervention, 2000 pp. 276-286
C.T. Zahn, "Graph-theoretical methods for detecting and describing gestalt clusters", IEEE Trans. Comput., vol. 100, pp. 68-86, 1971.
S.H. Kwok, and A.G. Constantinides, "A fast recursive shortest spanning tree for image segmentation and edge detection", IEEE Trans. Image Process., vol. 6, pp. 328-332, 1997.
P.F. Felzenszwalb, and D.P. Huttenlocher, "Efficient graph-based image segmentation", Int. J. Comput. Vis., vol. 59, pp. 167-181, 2004.
Y. Xu, and E.C. Uberbacher, "2D image segmentation using minimum spanning trees", Image Vis. Comput., vol. 15, pp. 47-57, 1997.
A.X. Falcão, and J.K. Udupa, "A 3D generalization of user-steered live-wire segmentation", Med. Image Anal., vol. 4, pp. 389-402, 2000.
A.X. Falcão, J. Stolfi, and R. de Alencar Lotufo, "The image foresting transform: Theory, algorithms, and applications", IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, pp. 19-29, 2004.
R. Ardon, and L.D. Cohen, "Fast constrained surface extraction by minimal paths", Int. J. Comput. Vis., vol. 69, pp. 127-136, 2006.
L. Grady, "Minimal surfaces extend shortest path segmentation methods to 3D", IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, pp. 321-334, 2010.
M. Sonka, M.D. Winniford, and S.M. Collins, "Robust simultaneous detection of coronary borders in complex images", IEEE Trans. Med. Imaging, vol. 14, pp. 151-161, 1995.
Y.Y. Boykov, and M-P. Jolly, "IInteractive graph cuts for optimal
boundary & region segmentation of objects in ND images", Computer
Vision, ICCV 2001. Proceedings. 8th IEEE International
Conference on, 2001 pp. 105-112
Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts", IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, pp. 1222-1239, 2001.
W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, "Random walk and graph cut for co-segmentation of lung tumor on PET-CT images", IEEE Trans. Image Process., vol. 24, pp. 5854-5867, 2015.
Y. Boykov, and V. Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision", IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, pp. 1124-1137, 2004.
I. Njeh, L. Sallemi, I.B. Ayed, K. Chtourou, S. Lehericy, D. Galanaud, and A.B. Hamida, "3D multimodal MRI brain glioma tumor and edema segmentation: A graph cut distribution matching approach", Comput. Med. Imaging Graph., vol. 40, pp. 108-119, 2015.
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, J.B. Freymann, K. Farahani, and C. Davatzikos, "Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features", Sci. Data, vol. 4, p. 170117, 2017.
B.H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, and L. Lanczi, "The multimodal brain tumor image segmentation benchmark (BRATS)", IEEE Trans. Med. Imaging, vol. 34, pp. 1993-2024, 2015.
D. Jyotsna, S. Jain, and M. Sood, "Segmentation of MR images using hybrid kmean-graph cut technique", Procedia Comput. Sci., vol. 132, pp. 775-784, 2018.
M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.M. Jodoin, and H. Larochelle, "Brain tumor segmentation with deep neural networks", Med. Image Anal., vol. 35, pp. 18-31, 2017.
D. Kwon, R.T. Shinohara, H. Akbari, and C. Davatzikos, "Combining generative models for multifocal glioma segmentation and registration", International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014 pp. 763-770