Recent Advancements in the Automatic Detection and Segmentation of GBMs from Multimodal Brain MRI Images

International Journal of Computer Science and Engineering
© 2015 by SSRG - IJCSE Journal
Volume 2 Issue 12
Year of Publication : 2015
Authors : A.Ratna Raju, P.Suresh, R.Rajeswara Rao

How to Cite?

A.Ratna Raju, P.Suresh, R.Rajeswara Rao, "Recent Advancements in the Automatic Detection and Segmentation of GBMs from Multimodal Brain MRI Images," SSRG International Journal of Computer Science and Engineering , vol. 2,  no. 12, pp. 19-23, 2015. Crossref,


Segmentation of tumors from multimodal MRI images is a challenging and time consuming task done manually by radiologists. Automation of this task is challenging because of the high variance in appearance of glial cells, among different patients and, similarity between tumor and normal tissue. In this paper we present the results of our survey on recent progress in the segmentation of brain tumors from multimodal MRI images Multimodal Brain Tumor Segmentation.


BRATs, Generative model, Discriminative model, SVMs.


[1] E. C. Holland, ―Progenitor cells and glioma formation, Current Opinion in Neurology, vol. 14, pp. 683–688, 2001.
[2] H. Ohgaki and P. Kleihues, ―Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J Neuropathol Exp Neurol, vol. 64, no. 6, pp. 479–489, Jun 2005.
[3] D. H. Louis, H. Ohgaki, O. D. Wiestler, and W. K. Cavanee, ―WHO classification of tumours of the central nervous system, WHO/IARC., Lyon, France, Tech. Rep., 2007.
[4] E. Eisenhauer, P. Therasse, J. Bogaerts, L. Schwartz, D. Sargent, R. Ford, J. Dancey, S. Arbuck, S. Gwyther, M. Mooney, L. Rubinstein, L. Shankar, L. Dodd, R. Kaplan, D. Lacombe, and J. Verweij, ―New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1), European Journal of Cancer, vol. 45, no. 2, pp. 228–247, 2009.
[5] 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. assman, C. Tsien, T. Mikkelsen, E. T. Wong, M. C. Chamberlain, R. Stupp, K. R. Lamborn, M. a. Vogelbaum, M. J. van den Bent, and S. M. Chang, ―Updated response assessment criteria for highgrade gliomas: response assessment in neuro-oncology working group. Journal of clinical oncology, vol. 28, pp.1963-72, 2010.
[6] D. A. Gutman, L. A. Cooper, S. N. Hwang, C. A. Holder, J. Gao, Aurora, W. D. Dunn, Jr., L. Scarpace, T. Mikkelsen, R. Jain, M. Wintermark, M. Jilwan, P. Raghavan, E. Huang, R. J. Clifford, P. Mongkolwat, V. Kleper, J. Freymann, J. Kirby, P. O. Zinn, C. S. Moreno, C. Jaffe, R. Colen, D. L. Rubin, J. Saltz, A. Flanders, and D. J. Brat, ―MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set, Radiology, vol. 267, no. 2, pp. 560–569, May 2013.
[7] Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS. Automatic tumor segmentation using knowledgebased techniques. IEEE Trans Med Imaging 1998;17(2):187– 201.
[8] Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR. Automatic segmentation of non-enhancing brain tumors. Artif Intell Med 2001;21:43–63.
[9] Dou W, Ruan S, Chen Y, Bloyet D, Constans J. A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR Images. Image Vision Comput 2007;25:164–71. [10] Dou W, Ruan S, Chen Y, Bloyet D, Constans J. A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR Images. Image Vision Comput 2007; 25:164–71.
[11] Prastawa M, Bullitt E, Ho S, Gerig G. A brain tumor segmentation framework based on outlier detection. Med Image Anal 2004;8: 275–83.
[12] Zhu Y, Yan H. Computerized tumor boundary detection using a Hopfield neural network. IEEE Trans Med Imaging 1997;16(1):55–67.
[13] Dickson S, Thomas BT, Goddard P. Using neural networks to automatically detect brain tumors in MR images. Int J Neural Syst 1997;8:91–9.
[14] Mancas M, Gosselin B. Iterative watersheds and fuzzy tumor visualization. 14th IEEE Visualization 2003:81–2.
[15] Letteboer MM, Olsen OF, Dam EB, Willems PWA, Viergever MA,Niessen WJ. Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm. Acad Radiol 2004;11(10):1125–38.
[16] Kass M, Witkin A, Terzopoulus D. Snakes: active contour model. Int J Comput Vision 1988;1(4):321–31.
[17] Caselles V. Geodesic active contours. Int J Comput Vision 1997;22 (3):61–79.
[18] Xu C, Prince JL. Snakes, shapes, and gradient vector flow. Int J Comput Vision 1998;7(3):359–69.
[19] Sum KW, Cheung PYS. Boundary vector field for parametric active contours. Pattern Recogn 2007;40(6):1635–45.
[20] Wang T, Cheng I, Basu A. Fluid vector flow and applications in brain tumor segmentation. IEEE Trans Biomed Eng 2009; 56(3):781–9.
[21] Malladi R, Sethian A, Vemuri Baba C. Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 1995;17(2):158–75.
[22] Juan O, Keriven R, Postelnicu G. Stochastic motion and the level set method in computer vision: stochastic active contour. Int J Comput Vision 2006;69(1):7–25.
[23] Xie X, Mirmehdi M. MAC: Magnetostatic active contour model. IEEE Trans Pattern Anal Mach Intell 2008;30(4):632– 45.
[24] D.Zikic,B.Glocker, E.Konukoglu, J.Shottan, A.Criminci, D.H.Ye, C.Demiralp,, O.M. Thomas, T.Das, R.Jena, S.J. Price, Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues, MICCAI proceedings, 2012.
[25] Stefan Bauer, Thomas Fejes, Johannes Slotboom, Roland Wiest, Lutz-P. Nolte, and Mauricio Reyes Segmentation of Brain Tumor mages Based on Integrated Hierarchical Classification and Regularization, MICCAI proceedings, 2012.
[26] E. Geremia, B. H. Menze, N. Ayache, Spatial Decision Forests for Glioma Segmentation in Multi-Channel MR Images, MICCAI proceedings, 2012.
[27] Hemamci, G.Unal, Multimodal Brain Tumor Segmentation Using The ―Tumor-cut Method on The BraTS Dataset, MICCAI proceedings, 2012.
[28] L. Zhao, W.Wu, J.J.Corso, Brain tumor segmentation based on GMM and active contour method with a model-aware edge map, MICCAI proceedings, 2012.
[29] N.K.Subbanna, T.Arbel, Probabilistic Gabor and Markov Random Fields Segmentation of Brain Tumours in MRI Volumes, MICCAI proceedings, 2012.
[30] Y. Xiao, J.Hu, Hierarchical Random Walker for Multimodal Brain Tumor Segmentation, MICCAI proceedings, 2012.
[31] Tomas Fernandez, S.K.Warfield, Automatic Brain Tumor Segmentation based on a Coupled Global-Local Intensity Bayesian Model, MICCAI proceedings, 2012.
[32] B.H.Menze, K. Van Leemput, D.Lashkari, M.A.webber, N.Ayache, P.Golland, Segmenting Glioma in Multi-Modal Images using a Generative Model for Brain Lesion Segmentation, MICCAI proceedings, 2012.