Journal Home > Volume 19 , Issue 6

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRI-based brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.


menu
Abstract
Full text
Outline
About this article

A Survey of MRI-Based Brain Tumor Segmentation Methods

Show Author's information Jin LiuMin LiJianxin Wang( )Fangxiang WuTianming LiuYi Pan
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
Department of Computer Science, University of Georgia, Boyd 420, Athens, GA 30602, USA.
Department of Computer Science, Georgia State University, Atlanta, GA 30302-3994, USA.

Abstract

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRI-based brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.

Keywords: Magnetic Resonance Imaging (MRI), segmentation, brain tumor

References(149)

[1]
M. P. Gupta and M. M. Shringirishi, Implementation of brain tumor segmentation in brain mr images using k-means clustering and fuzzy c-means algorithm, International Journal of Computers & Technology, vol. 5, no. 1, pp. 54-59, 2013.
[2]
D. N. Louis, H. Ohgaki, O. D. Wiestler, W. K. Cavenee, P. C. Burger, A. Jouvet, B. W. Scheithauer, and P. Kleihues, The 2007 who classification of tumours of the central nervous system, Acta Neuropathologica, vol. 114, no. 2, pp. 97-109, 2007.
[3]
Z.-P. Liang and P. C. Lauterbur, Principles of Magnetic Resonance Imaging: A Signal Processing Perspective. The Institute of Electrical and Electronics Engineers Press, 2000.
DOI
[4]
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, et al., Updated response assessment criteria for high-grade gliomas: Response assessment in neuro-oncology working group, Journal of Clinical Oncology, vol. 28, no. 11, pp. 1963-1972, 2010.
[5]
A. Drevelegas and N. Papanikolaou, Imaging modalities in brain tumors, in Imaging of Brain Tumors with Histological Correlations. Springer, 2011, pp. 13-33.
DOI
[6]
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, Medical Imaging, IEEE Transactions on, vol. 27, no. 5, pp. 629-640, 2008.
[7]
Y.-L. You, W. Xu, A. Tannenbaum, and M. Kaveh, Behavioral analysis of anisotropic diffusion in image processing, Image Processing, IEEE Transactions on, vol. 5, no. 11, pp. 1539-1553, 1996.
[8]
J. Weickert, Anisotropic Diffusion in Image Processing, vol. 1. Teubner Stuttgart, 1998.
[9]
T. Ogden, Essential Wavelets for Statistical Applications and Data Analysis. Springer, 1997.
DOI
[10]
R. D. Nowak, Wavelet-based rician noise removal for magnetic resonance imaging, Image Processing, IEEE Transactions on, vol. 8, no. 10, pp. 1408-1419, 1999.
[11]
A. Buades, B. Coll, and J.-M. Morel, A non-local algorithm for image denoising, in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, IEEE, 2005, vol. 2, pp. 60-65.
[12]
J. V. Manjón, P. Coupé, L. Martí-Bonmatí, D. L. Collins, and M. Robles, Adaptive non-local means denoising of mr images with spatially varying noise levels, Journal of Magnetic Resonance Imaging, vol. 31, no. 1, pp. 192-203, 2010.
[13]
S. Prima and O. Commowick, Using bilateral symmetry to improve non-local means denoising of mr brain images, in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, IEEE, 2013, pp. 1231-1234.
DOI
[14]
P. Hoyer, Independent component analysis in image denoising, Master degree dissertation, Helsinki University of Technology, 1999.
[15]
K. Phatak, S. Jakhade, A. Nene, R. Kamathe, and K. Joshi, De-noising of magnetic resonance images using independent component analysis, in Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE, IEEE, 2011, pp. 807-812.
DOI
[16]
I. Diaz, P. Boulanger, R. Greiner, and A. Murtha, A critical review of the effects of de-noising algorithms on mri brain tumor segmentation, in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, IEEE, 2011, pp. 3934-3937.
DOI
[17]
C. Fennema-Notestine, I. B. Ozyurt, C. P. Clark, S. Morris, A. Bischoff-Grethe, M. W. Bondi, T. L. Jernigan, B. Fischl, F. Segonne, D. W. Shattuck, et al., Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location, Human Brain Mapping, vol. 27, no. 2, pp. 99-113, 2006.
[18]
A. H. Zhuang, D. J. Valentino, and A. W. Toga, Skull-stripping magnetic resonance brain images using a model-based level set, NeuroImage, vol. 32, no. 1, pp. 79-92, 2006.
[19]
R. Roslan, N. Jamil, and R. Mahmud, Skull stripping magnetic resonance images brain images: Region growing versus mathematical morphology, International Journal of Computer Information Systems and Industrial Management Applications, vol. 3, pp. 150-158, 2011.
[20]
S. Bauer, L.-P. Nolte, and M. Reyes, Skull-stripping for tumor-bearing brain images, arXiv preprint arXiv: 1204.0357, 2012.
DOI
[21]
S. F. Eskildsen, P. Coupé, V. Fonov, J. V. Manjón, K. K. Leung, N. Guizard, S. N. Wassef, L. R. Østergaard, and D. L. Collins, Beast: Brain extraction based on nonlocal segmentation technique, NeuroImage, vol. 59, no. 3, pp. 2362-2373, 2012.
[22]
F. Ségonne, A. Dale, E. Busa, M. Glessner, D. Salat, H. Hahn, and B. Fischl, A hybrid approach to the skull stripping problem in mri, Neuroimage, vol. 22, no. 3, pp. 1060-1075, 2004.
[23]
N. F. Ishak, R. Logeswaran, and W.-H. Tan, Artifact and noise stripping on low-field brain mri, Int. J. Biology Biomed. Eng, vol. 2, no. 2, pp. 59-68, 2008.
[24]
S. Shen, W. Sandham, M. Granat, and A. Sterr, Mri fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization, Information Technology in Biomedicine, IEEE Transactions on, vol. 9, no. 3, pp. 459-467, 2005.
[25]
J. G. Park and C. Lee, Skull stripping based on region growing for magnetic resonance brain images, NeuroImage, vol. 47, no. 4, pp. 1394-1407, 2009.
[26]
W. Speier, J. E. Iglesias, L. El-Kara, Z. Tu, and C. Arnold, Robust skull stripping of clinical glioblastoma multiforme data, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011, Springer, 2011, pp. 659-666.
DOI
[27]
L. G. Nyu and J. K. Udupa, On standardizing the mr image intensity scale, Image, vol. 1081, 1999.
[28]
A. Ekin, Pathology-robustmr intensity normalizationwith global and local constraints, in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, IEEE, 2011, pp. 333-336.
DOI
[29]
M. Shah, Y. Xiao, N. Subbanna, S. Francis, D. L. Arnold, D. L. Collins, and T. Arbel, Evaluating intensity normalization on mris of human brain with multiple sclerosis, Medical Image Analysis, vol. 15, no. 2, pp. 267-282, 2011.
[30]
A. Mang, J. A. Schnabel, W. R. Crum, M. Modat, O. Camara-Rey, C. Palm, G. B. Caseiras, H. R. Jäger, S. Ourselin, T. M. Buzug, et al., Consistency of parametric registration in serial mri studies of brain tumor progression, International Journal of Computer Assisted Radiology and Surgery, vol. 3, nos. 3-4, pp. 201-211, 2008.
[31]
D. L. Pham, C. Xu, and J. L. Prince, Current methods in medical image segmentation 1, Annual Review of Biomedical Engineering, vol. 2, no. 1, pp. 315-337, 2000.
[32]
N. Sharma and L. M. Aggarwal, Automated medical image segmentation techniques, Journal of Medical Physics/Association of Medical Physicists of India, vol. 35, no. 1, p. 3, 2010.
[33]
S. Bauer, R. Wiest, L.-P. Nolte, and M. Reyes, A survey of mri-based medical image analysis for brain tumor studies, Physics in Medicine and Biology, vol. 58, no. 13, p. R97, 2013.
[34]
H. Suzuki and J.-I. Toriwaki, Automatic segmentation of head mri images by knowledge guided thresholding, Computerized Medical Imaging and Graphics, vol. 15, no. 4, pp. 233-240, 1991.
[35]
G. Harris, P. Barta, L. Peng, S. Lee, P. Brettschneider, A. Shah, J. Henderer, T. Schlaepfer, and G. Pearlson, MR volume segmentation of gray matter and white matter using manual thresholding: Dependence on image brightness, American Journal of Neuroradiology, vol. 15, no. 2, pp. 225-230, 1994.
[36]
R. Adams and L. Bischof, Seeded region growing, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, no. 6, pp. 641-647, 1994.
[37]
C. Vijayakumar and D. C. Gharpure, Development of image-processing software for automatic segmentation of brain tumors in mr images, Journal of Medical Physics/Association of Medical Physicists of India, vol. 36, no. 3, p. 147, 2011.
[38]
Y.-C. Sung, K.-S. Han, C.-J. Song, S.-M. Noh, and J.-W. Park, Threshold estimation for region segmentation on mr image of brain having the partial volume artifact, in Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on, IEEE, 2000, vol. 2, pp. 1000-1009.
[39]
A. Stadlbauer, E. Moser, S. Gruber, R. Buslei, C. Nimsky, R. Fahlbusch, and O. Ganslandt, Improved delineation of brain tumors: An automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomas, Neuroimage, vol. 23, no. 2, pp. 454-461, 2004.
[40]
K.-P. Wong, Medical image segmentation: Methods and applications in functional imaging, in Handbook of Biomedical Image Analysis. Springer, 2005, pp. 111-182.
[41]
G. Mittelhaeusser and F. Kruggel, Fast segmentation of brain magnetic resonance tomograms, in Computer Vision, Virtual Reality and Robotics in Medicine. Springer, 1995, pp. 237-241.
DOI
[42]
M. R. Kaus, S. K. Warfield, A. Nabavi, P. M. Black, F. A. Jolesz, and R. Kikinis, Automated segmentation of mr images of brain tumors 1, Radiology, vol. 218, no. 2, pp. 586-591, 2001.
[43]
V. F. Chong, J.-Y. Zhou, J. B. Khoo, J. Huang, and T.-K. Lim, Tongue carcinoma: Tumor volume measurement, International Journal of Radiation Oncology Biology Physics, vol. 59, no. 1, pp. 59-66, 2004.
[44]
Y. Salman, M. Assal, A. Badawi, S. Alian, and M. E. El-Bayome, Validation techniques for quantitative brain tumors measurements, in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, IEEE, 2006, pp. 7048-7051.
DOI
[45]
M. Sato, S. Lakare, M. Wan, A. Kaufman, and M. Nakajima, A gradient magnitude based region growing algorithm for accurate segmentation, in Image Processing, 2000. Proceedings. 2000 International Conference on, IEEE, 2000, vol. 3, pp. 448-451.
[46]
S. Lakare and A. Kaufman, 3D segmentation techniques for medical volumes, Center for Visual Computing, Department of Computer Science, State University of New York, 2000.
[47]
Y. M. Salman, Modified technique for volumetric brain tumor measurements, Journal of Biomedical Science and Engineering, vol. 2, p. 16, 2009.
[48]
W. Dou, S. Ruan, Y. Chen, D. Bloyet, and J.-M. Constans, A framework of fuzzy information fusion for the segmentation of brain tumor tissues on mr images, Image and Vision Computing, vol. 25, no. 2, pp. 164-171, 2007.
[49]
M. Letteboer, W. Niessen, P. Willems, E. B. Dam, and M. Viergever, Interactive multi-scale watershed segmentation of tumors in mr brain images, in Proc. of the IMIVA Workshop of MICCAI, Citeseer, 2001.
[50]
E. Dam, M. Loog, and M. Letteboer, Integrating automatic and interactive brain tumor segmentation, in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, IEEE, 2004, vol. 3, pp. 790-793.
DOI
[51]
J. E. Cates, R. T. Whitaker, and G. M. Jones, Case study: An evaluation of user-assisted hierarchical watershed segmentation, Medical Image Analysis, vol. 9, no. 6, pp. 566-578, 2005.
[52]
R. Ratan, S. Sharma, and S. Sharma, Multiparameter segmentation and quantization of brain tumor from mri images, Indian Journal of Science and Technology, vol. 2, no. 2, pp. 11-15, 2009.
[53]
S. D. Salman and A. A. Bahrani, Segmentation of tumor tissue in gray medical images using watershed transformation methods, International Journal of Advancements in Computing Technology, vol. 2, no. 4, pp. 123-127, 2010.
[54]
A. Bleau and L. J. Leon, Watershed-based segmentation and region merging, Computer Vision and Image Understanding, vol. 77, no. 3, pp. 317-370, 2000.
[55]
V. Gies and T. M. Bernard, Statistical solution to watershed over-segmentation, in International Conference on Image Processing, 2004.
[56]
J. Kong, J. Wang, Y. Lu, J. Zhang, Y. Li, and B. Zhang, A novel approach for segmentation of mri brain images, in Electrotechnical Conference, 2006. MELECON 2006. IEEE Mediterranean, IEEE, 2006, pp. 525-528.
[57]
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. John Wiley & Sons, 2012.
[58]
C. M. Bishop, Pattern Recognition and Machine Learning, vol. 1. Springer New York, 2006.
[59]
T. M. Mitchell, The discipline of machine learning, Carnegie Mellon University, School of Computer Science, 2006.
[60]
E. Alpaydin, Introduction to Machine Learning. MIT Press, 2004.
[61]
T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, The elements of statistical learning: Data mining, inference and prediction, The Mathematical Intelligencer, vol. 27, no. 2, pp. 83-85, 2005.
[62]
O. Chapelle, B. Schölkopf, and A. Zien, ed., Semi-Supervised Learning, vol. 2. MIT Press Cambridge, 2006.
DOI
[63]
C. Christakou, L. Lefakis, S. Vrettos, and A. Stafylopatis, A movie recommender system based on semi-supervised clustering, in Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on, IEEE, 2005, vol. 2, pp. 897-903.
DOI
[64]
J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, 1981.
DOI
[65]
L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, and J. C. Bezdek, A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain, Neural Networks, IEEE Transactions on, vol. 3, no. 5, pp. 672-682, 1992.
[66]
W. Phillips II, R. Velthuizen, S. Phuphanich, L. Hall, L. Clarke, and M. Silbiger, Application of fuzzy c-means segmentation technique for tissue differentiation in mr images of a hemorrhagic glioblastoma multiforme, Magnetic Resonance Imaging, vol. 13, no. 2, pp. 277-290, 1995.
[67]
M. C. Clark, L. O. Hall, D. B. Goldgof, R. Velthuizen, F. R. Murtagh, and M. S. Silbiger, Automatic tumor segmentation using knowledge-based techniques, Medical Imaging, IEEE Transactions on, vol. 17, no. 2, pp. 187-201, 1998.
[68]
L. M. Fletcher-Heath, L. O. Hall, D. B. Goldgof, and F. R. Murtagh, Automatic segmentation of non-enhancing brain tumors in magnetic resonance images, Artificial Intelligence in Medicine, vol. 21, no. 1, pp. 43-63, 2001.
[69]
G.-C. Lin, W.-J. Wang, C.-C. Kang, and C.-M. Wang, Multispectral mr images segmentation based on fuzzy knowledge and modified seeded region growing, Magnetic Resonance Imaging, vol. 30, no. 2, pp. 230-246, 2012.
[70]
L. Szilagyi, Z. Benyo, S. M. Szilágyi, and H. Adam, Mr brain image segmentation using an enhanced fuzzy c-means algorithm, in Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE, IEEE, 2003, vol. 1, pp. 724-726.
[71]
W. Cai, S. Chen, and D. Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition, vol. 40, no. 3, pp. 825-838, 2007.
[72]
L. Szilágyi, S. M. Szilágyi, and Z. Benyó, A modified fuzzy c-means algorithm for mr brain image segmentation, in Image Analysis and Recognition. Springer, 2007, pp. 866-877.
[73]
M. Forouzanfar, N. Forghani, and M. Teshnehlab, Parameter optimization of improved fuzzy c-means clustering algorithm for brain mr image segmentation, Engineering Applications of Artificial Intelligence, vol. 23, no. 2, pp. 160-168, 2010.
[74]
J. Maintz and M. A. Viergever, A survey of medical image registration, Medical Image Analysis, vol. 2, no. 1, pp. 1-36, 1998.
[75]
M. B. Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, and J. Thiran, Atlas-based segmentation of pathological mr brain images using a model of lesion growth, Medical Imaging, IEEE Transactions on, vol. 23, no. 10, pp. 1301-1314, 2004.
[76]
A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society, vol. 39, no. 1, pp. 1-38, 1977.
[77]
N. Moon, E. Bullitt, K. Van Leemput, and G. Gerig, Automatic brain and tumor segmentation, in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2002. Springer, 2002, pp. 372-379.
DOI
[78]
M. Prastawa, E. Bullitt, N. Moon, K. Van Leemput, and G. Gerig, Automatic brain tumor segmentation by subject specific modification of atlas priors, Academic Radiology, vol. 10, no. 12, pp. 1341-1348, 2003.
[79]
A. Mohamed, D. Shen, and C. Davatzikos, Deformable registration of brain tumor images via a statistical model of tumor-induced deformation, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2005. Springer, 2005, pp. 263-270.
DOI
[80]
T. Liu, D. Shen, and C. Davatzikos, Deformable registration of tumor-diseased brain images, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2004. Springer, 2004, pp. 720-728.
DOI
[81]
F. Shi, P.-T. Yap, Y. Fan, J. H. Gilmore, W. Lin, and D. Shen, Construction of multi-region-multi-reference atlases for neonatal brain mri segmentation, Neuroimage, vol. 51, no. 2, pp. 684-693, 2010.
[82]
B. H. Menze, K. Van Leemput, D. Lashkari, M.-A. Weber, N. Ayache, and P. Golland, A generative model for brain tumor segmentation in multi-modal images, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2010. Springer, 2010, pp. 151-159.
DOI
[83]
M. Cabezas, A. Oliver, X. Lladó, J. Freixenet, and M. Bach Cuadra, A review of atlas-based segmentation for magnetic resonance brain images, Computer Methods and Programs in Biomedicine, vol. 104, no. 3, pp. e158-e177, 2011.
[84]
L. Weizman, L. Ben Sira, L. Joskowicz, S. Constantini, R. Precel, B. Shofty, and D. Ben Bashat, Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in mri, Medical Image Analysis, vol. 16, no. 1, pp. 177-188, 2012.
[85]
K. Brodmann, Vergleichende Lokalisationslehre der Gro hirnrinde. Springer, 1909.
[86]
J. Talairach and P. Tournoux, Co-Planar Stereotaxic Atlas of the Human Brain: 3-dimensional proportation System—An Approach to Cerebral Imaging. New York, USA: Thieme Medical Publishers, 1988.
[87]
A. Evans, S. Marrett, J. Torrescorzo, S. Ku, and L. Collins, Mri-pet correlation in three dimensions using a volume-of-interest (voi) atlas, Journal of Cerebral Blood Flow & Metabolism, vol. 11, pp. A69-A78, 1991.
[88]
M. Shenton, R. Kikinis, W. McCarley, P. Saiviroonporn, H. Hokama, A. Robatino, D. Metcalf, C. Wible, C. Portas, D. Iosifescu, et al., Harvard brain atlas: A teaching and visualization tool, in Proceedings of the 1995 Biomedical Visualization, 1995, pp. 10-17.
[89]
R. Kindermann and J. L. Snell, Markov Random Fields and Their Applications, vol. 1. American Mathematical Society Providence, RI, 1980.
DOI
[90]
T. N. Tran, R. Wehrens, and L. Buydens, Clustering multispectral images: A tutorial, Chemometrics and Intelligent Laboratory Systems, vol. 77, no. 1, pp. 3-17, 2005.
[91]
J. Lafferty, A. McCallum, and F. C. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, in Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001), 2001.
[92]
A.-S. Capelle, O. Alata, C. Fernandez, S. Lefèvre, and J. Ferrie, Unsupervised segmentation for automatic detection of brain tumors in mri, in Image Processing, 2000. Proceedings. 2000 International Conference on, IEEE, 2000, vol. 1, pp. 613-616.
[93]
D. T. Gering, W. E. L. Grimson, and R. Kikinis, Recognizing Deviations from Normalcy for Brain Tumor Segmentation. Springer, 2002.
DOI
[94]
J. Nie, Z. Xue, T. Liu, G. S. Young, K. Setayesh, L. Guo, and S. T. Wong, Automated brain tumor segmentation using spatial accuracy-weighted hidden markov random field, Computerized Medical Imaging and Graphics, vol. 33, no. 6, pp. 431-441, 2009.
[95]
S. Bauer, L.-P. Nolte, and M. Reyes, Segmentation of brain tumor images based on atlas-registration combined with a markov-random-field lesion growth model, in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, IEEE, 2011, pp. 2018-2021.
DOI
[96]
N. K. Subbanna, D. Precup, D. L. Collins, and T. Arbel, Hierarchical probabilistic gabor and mrf segmentation of brain tumours in mri volumes, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013. Springer, 2013, pp. 751-758.
DOI
[97]
C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[98]
V. Vapnik, The Nature of Statistical Learning Theory. Springer, 2000.
DOI
[99]
J. Zhou, K. Chan, V. Chong, and S. Krishnan, Extraction of brain tumor from mr images using one-class support vector machine, in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the, IEEE, 2006, pp. 6411-6414.
DOI
[100]
H. Cai, R. Verma, Y. Ou, S.-K. Lee, E. R. Melhem, and C. Davatzikos, Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images, in Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on, IEEE, 2007, pp. 600-603.
DOI
[101]
R. Verma, E. I. Zacharaki, Y. Ou, H. Cai, S. Chawla, S.-K. Lee, E. R. Melhem, R. Wolf, and C. Davatzikos, Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of mr images, Academic Radiology, vol. 15, no. 8, pp. 966-977, 2008.
[102]
S. Ruan, S. Lebonvallet, A. Merabet, and J. Constans, Tumor segmentation from a multispectral mri images by using support vector machine classification, in Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on, IEEE, 2007, pp. 1236-1239.
DOI
[103]
S. Ruan, N. Zhang, Q. Liao, and Y. Zhu, Image fusion for following-up brain tumor evolution, in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on, IEEE, 2011, pp. 281-284.
DOI
[104]
N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao, and Y. Zhu, Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence, in Image Processing (ICIP), 2009 16th IEEE International Conference on, IEEE, 2009, pp. 3373-3376.
[105]
S. Bauer, L.-P. Nolte, and M. Reyes, Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011. Springer, 2011, pp. 354-361.
DOI
[106]
D. Zikic, B. Glocker, E. Konukoglu, A. Criminisi, C. Demiralp, J. Shotton, O. Thomas, T. Das, R. Jena, and S. Price, Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel mr, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012. Springer, 2012, pp. 369-376.
DOI
[107]
N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao, and Y. Zhu, Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation, Computer Vision and Image Understanding, vol. 115, no. 2, pp. 256-269, 2011.
[108]
H. Delingette, M. Hebert, and K. Ikeuchi, Shape representation and image segmentation using deformable surfaces, Image and Vision Computing, vol. 10, no. 3, pp. 132-144, 1992.
[109]
D. García-Lorenzo, S. Francis, S. Narayanan, D. L. Arnold, and D. L. Collins, Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging, Medical Image Analysis, vol. 17, no. 1, pp. 1-18, 2013.
[110]
H. Tek and B. B. Kimia, Shock-based reaction-diffusion bubbles for image segmentation, in Computer Vision, Virtual Reality and Robotics in Medicine. Springer, 1995, pp. 434-438.
DOI
[111]
T. McInerney and D. Terzopoulos, Deformable models in medical image analysis: A survey, Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.
[112]
M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, vol. 1, no. 4, pp. 321-331, 1988.
[113]
C. Xu and J. L. Prince, Snakes, shapes, and gradient vector flow, Image Processing, IEEE Transactions on, vol. 7, no. 3, pp. 359-369, 1998.
[114]
A. Singh, D. Terzopoulos, and D. B. Goldgof, Deformable Models in Medical Image Analysis. IEEE Computer Society Press, 1998.
[115]
T. F. Chan and L. A. Vese, Active contours without edges, Image Processing, IEEE Transactions on, vol. 10, no. 2, pp. 266-277, 2001.
[116]
S. Luo, R. Li, and S. Ourselin, A new deformable model using dynamic gradient vector flow and adaptive balloon forces, in APRS Workshop on Digital Image Computing, 2003, pp. 9-14.
[117]
H. Khotanlou, O. Colliot, J. Atif, and I. Bloch, 3D brain tumor segmentation in mri using fuzzy classification, symmetry analysis and spatially constrained deformable models, Fuzzy Sets and Systems, vol. 160, no. 10, pp. 1457-1473, 2009.
[118]
A. Gooya, K. M. Pohl, M. Bilello, G. Biros, and C. Davatzikos, Joint segmentation and deformable registration of brain scans guided by a tumor growth model, in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011. Springer, 2011, pp. 532-540.
DOI
[119]
R. Malladi, J. A. Sethian, and B. C. Vemuri, Shape modeling with front propagation: A level set approach, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 17, no. 2, pp. 158-175, 1995.
[120]
S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Gradient flows and geometric active contour models, in Computer Vision, 1995. Proceedings., Fifth International Conference on, IEEE, 1995, pp. 810-815.
[121]
S. Ho, E. Bullitt, and G. Gerig, Level-set evolution with region competition: Automatic 3-D segmentation of brain tumors, in Pattern Recognition, 2002. Proceedings. 16th International Conference on, IEEE, 2002, vol. 1, pp. 532-535.
[122]
M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, A brain tumor segmentation framework based on outlier detection, Medical Image Analysis, vol. 8, no. 3, pp. 275-283, 2004.
[123]
H.-H. Chang and D. J. Valentino, Image segmentation using a charged fluid method, Journal of Electronic Imaging, vol. 15, no. 2, pp. 023011-023011, 2006.
[124]
H.-H. Chang and D. J. Valentino, An electrostatic deformable model for medical image segmentation, Computerized Medical Imaging and Graphics, vol. 32, no. 1, pp. 22-35, 2008.
[125]
C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, Minimization of region-scalable fitting energy for image segmentation, Image Processing, IEEE Transactions on, vol. 17, no. 10, pp. 1940-1949, 2008.
[126]
C. Li, R. Huang, Z. Ding, J. Gatenby, D. N. Metaxas, and J. C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri, Image Processing, IEEE Transactions on, vol. 20, no. 7, pp. 2007-2016, 2011.
[127]
A. Hamamci, N. Kucuk, K. Karaman, K. Engin, and G. Unal, Tumor-cut: Segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications, Medical Imaging, IEEE Transactions on, vol. 31, no. 3, pp. 790-804, 2012.
[128]
W. R. Crum, O. Camara, and D. L. Hill, Generalized overlap measures for evaluation and validation in medical image analysis, Medical Imaging, IEEE Transactions on, vol. 25, no. 11, pp. 1451-1461, 2006.
[129]
N. Archip, F. A. Jolesz, and S. K. Warfield, A validation framework for brain tumor segmentation, Academic Radiology, vol. 14, no. 10, pp. 1242-1251, 2007.
[130]
M. Prastawa, E. Bullitt, and G. Gerig, Simulation of brain tumors in mr images for evaluation of segmentation efficacy, Medical Image Analysis, vol. 13, no. 2, pp. 297-311, 2009.
[131]
B. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, et al., The multimodal brain tumor image segmentation benchmark (brats), http://hal.inria.fr/hal-00935640, 2014.
[132]
M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, Fsl, Neuroimage, vol. 62, no. 2, pp. 782-790, 2012.
[133]
A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, et al., 3d slicer as an image computing platform for the quantitative imaging network, Magnetic Resonance Imaging, vol. 30, no. 9, pp. 1323-1341, 2012.
[134]
N. Toussaint, J.-C. Souplet, and P. Fillard, Medinria: Medical image navigation and research tool by inria, in Proc. of MICCAI, vol. 7, 2007.
[135]
D. Yang, J. Zheng, A. Nofal, J. Deasy, and I. M. El Naqa, Techniques and software tool for 3d multimodality medical image segmentation, Journal of Radiation Oncology Informatics, vol. 1, no. 1, pp. 1-22, 2009.
[136]
A. M. Dale, B. Fischl, and M. I. Sereno, Cortical surface-based analysis: I. segmentation and surface reconstruction, Neuroimage, vol. 9, no. 2, pp. 179-194, 1999.
[137]
I. Wolf, M. Vetter, I. Wegner, M. Nolden, T. Bottger, M. Hastenteufel, M. Schobinger, T. Kunert, and H.-P. Meinzer, The medical imaging interaction toolkit (mitk): A toolkit facilitating the creation of interactive software by extending vtk and itk, in Medical Imaging 2004, International Society for Optics and Photonics, 2004, pp. 16-27.
[138]
S. Bauer, T. Fejes, J. Slotboom, R. Wiest, L.-P. Nolte, and M. Reyes, Segmentation of brain tumor images based on integrated hierarchical classification and regularization, in MICCAI BraTS Workshop, 2012.
[139]
C. A. Cocosco, V. Kollokian, R. K.-S. Kwan, G. B. Pike, and A. C. Evans, Brainweb: Online interface to a 3D MRI simulated brain database, in NeuroImage, Citeseer, 1997.
[140]
S. Valverde, A. Oliver, M. Cabezas, E. Roura, and X. Lladó, Comparison of 10 brain tissue segmentation methods using revisited ibsr annotations, Journal of Magnetic Resonance Imaging, 2014. .
[141]
S. Cha, Update on brain tumor imaging: From anatomy to physiology, American Journal of Neuroradiology, vol. 27, no. 3, pp. 475-487, 2006.
[142]
M. Law, Advanced imaging techniques in brain tumors, Cancer Imaging, vol. 9, no. Special issue A, p. S4, 2009.
[143]
E. I. Zacharaki, S. Wang, S. Chawla, D. Soo Yoo, R. Wolf, E. R. Melhem, and C. Davatzikos, Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme, Magnetic Resonance in Medicine, vol. 62, no. 6, pp. 1609-1618, 2009.
[144]
J. Sedlacik, A. Winchell, M. Kocak, R. Loeffler, A. Broniscer, and C. Hillenbrand, MR imaging assessment of tumor perfusion and 3d segmented volume at baseline, during treatment, and at tumor progression in children with newly diagnosed diffuse intrinsic pontine glioma, American Journal of Neuroradiology, vol. 34, no. 7, pp. 1450-1455, 2013.
[145]
B. Lemasson, T. L. Chenevert, T. S. Lawrence, C. Tsien, P. C. Sundgren, C. R. Meyer, L. Junck, J. Boes, S. Galbán, T. D. Johnson, et al., Impact of perfusion map analysis on early survival prediction accuracy in glioma patients, Translational Oncology, vol. 6, no. 6, p. 766, 2013.
[146]
A. L. Alexander, J. E. Lee, M. Lazar, and A. S. Field, Diffusion tensor imaging of the brain, Neurotherapeutics, vol. 4, no. 3, pp. 316-329, 2007.
[147]
E. Stretton, E. Geremia, B. Menze, H. Delingette, and N. Ayache, Importance of patient dti’s to accurately model glioma growth using the reaction diffusion equation, in Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, IEEE, 2013, pp. 1142-1145.
[148]
W. Dou, A. Dong, P. Chi, S. Li, and J. Constans, Brain tumor segmentation through data fusion of t2-weighted image and MR spectroscopy, in Bioinformatics and Biomedical Engineering,(iCBBE) 2011 5th International Conference on, IEEE, 2011, pp. 1-4.
[149]
J. Huo, K. Okada, E. M. van Rikxoort, H. J. Kim, J. R. Alger, W. B. Pope, J. G. Goldin, and M. S. Brown, Ensemble segmentation for gbm brain tumors on MR images using confidence-based averaging, Medical Physics, vol. 40, no. 9, p. 093502, 2013.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 16 June 2014
Accepted: 23 June 2014
Published: 20 November 2014
Issue date: December 2014

Copyright

The Author(s)

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos. 61232001 and 61379108).

Rights and permissions

Return