698
Views
69
Downloads
15
Crossref
N/A
WoS
18
Scopus
N/A
CSCD
Brain tumor extraction is challenging task because brain image and its structure are complicated that can be analyzed only by expert physicians or radiologist. Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. MRI (Magnetic Resonance Imaging) is a visualization medical technique, which provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumor. Accurate segmentation of the MRI images is extremely important and essential for the exact diagnosis by computer aided clinical tools. There are different types of segmentation algorithms for MRI brain images. This paper is to check existing approaches of Brain tumor segmentation techniques in MRI image for Computer aided diagnosis.
Brain tumor extraction is challenging task because brain image and its structure are complicated that can be analyzed only by expert physicians or radiologist. Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. MRI (Magnetic Resonance Imaging) is a visualization medical technique, which provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumor. Accurate segmentation of the MRI images is extremely important and essential for the exact diagnosis by computer aided clinical tools. There are different types of segmentation algorithms for MRI brain images. This paper is to check existing approaches of Brain tumor segmentation techniques in MRI image for Computer aided diagnosis.
A. Singh, S. Bajpai, S. Karanam, et al., Malignant brain tumor detection. Int. J. Comput. Theory Eng., 2012, 4(6): 1002.
K.M. Mahesh, J.A. Renjit, Evolutionary intelligence for brain tumor recognition from MRI images: A critical study and review. Evol. Intell., 2018, 11(1-2): 19-30.
M.S.H. Al-Tamimi, G. Sulong, Tumor brain detection through MR images: A review of literature. J. Theor. Appl. Inf. Technol., 2014, 62(2).
D. Mortazavi, A.Z. Kouzani, and H. Soltanian-Zadeh, Segmentation of multiple sclerosis lesions in MR images: A review. Neuroradiology, 2012, 54(4): 299-320.
X. Lladó, Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches. Inf Sci, 2012, 186(1): 164-185.
D. García-Lorenzo, S. Francis, S. Narayanan, et al., Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med. Image Anal., 2013, 17(1): 1-18.
S. Bauer, R. Wiest, L. -P. Nolte, et al., A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol., 2013, 58(13): R97.
E. -S.A. El-Dahshan, H.M. Mohsen, K. Revett, et al., Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst. Appl., 2014, 41(11): 5526-5545.
N. Singh, N. Choudhary, A survey: Brain tumor detection techniques of Computer aided diagnosis through MRI image. Int. J. Comput. Sci. Issues IJCSI, 2015, 12(6): 148.
A. Danelakis, T. Theoharis, and D.A. Verganelakis, Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging. Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc., 2018, 70: 83-100.
S. Iqbal, M.U.G. Khan, T. Saba, et al., Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed. Eng. Lett., 2018, 8(1): 5-28.
G. Mohan, M.M. Subashini, MRI based medical image analysis: Survey on brain tumor grade classification. Biomed. Signal Process. Control, 2018, 39: 139-161.
S. Yazdani, R. Yusof, A. Karimian, et al., Image segmentation methods and applications in MRI brain images. IETE Tech. Rev., 2015, 32(6): 413-427.
C. Fernandez-Lozano, J.A. Seoane, M. Gestal, et al., Texture classification using feature selection and kernel-based techniques. Soft Comput., 2015, 19(9): 2469-2480.
P.S.S. Kumar, A study of MRI segmentation methods in automatic brain tumor detection. Int. J. Eng. Technol, 2016, 8: 609-614.
N. Gordillo, E. Montseny, and P. Sobrevilla, State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging, 2013, 31(8): 1426-1438.
A.A. Taha, A. Hanbury, Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med. Imaging, 2015, 15(1): 29.
A. Rajendran, R. Dhanasekaran, Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: A combined approach. Procedia Eng., 2012, 30: 327-333.
J. Sachdeva, V. Kumar, I. Gupta, et al., A novel content-based active contour model for brain tumor segmentation. Magn. Reson. Imaging, 2012, 30(5): 694-715.
B. Tanoori, Z. Azimifar, A. Shakibafar, et al., Brain volumetry: An active contour model-based segmentation followed by SVM-based classification. Comput. Biol. Med., 2011, 41(8): 619-632.
P.C. Barman, M.S. Miah, B.C. Singh, et al., MRI image segmentation using level set method and implement an medical diagnosis system. Comput. Sci. Eng., 2011, 1(5): 1.
L. Pei, S.M.S. Reza, W. Li, et al., Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. Proc. SPIE-Int. Soc. Opt. Eng., 2017, 10134.
K. Somasundaram, T. Kalaiselvi, Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images. Comput. Biol. Med., 2010, 40(10): 811-822.
A. Aslam, E. Khan, and M.M.S. Beg, Improved edge detection algorithm for brain tumor segmentation. Procedia Comput. Sci., 2015, 58: 430-437.
R. Donoso, A. Veloz, and H. Allende, Modified expectation maximization algorithm for MRI segmentation. Progress in pattern recognition, image analysis, computer vision, and applications, 2010: 63-70.
V. Pedoia, S. Balbi, and E. Binaghi, Fully automatic brain tumor segmentation by using competitive EM and graph cut. Image analysis and processing–ICIAP, 2015: 568-578.
S. Yousefi, R. Azmi, and M. Zahedi, Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms. Med. Image Anal., 2012, 16(4): 840-848.
H. Merisaari, Gaussian mixture model-based segmentation of MR images taken from premature infant brains. J. Neurosci. Methods, 2009, 182(1): 110-122.
M. Maitra, A. Chatterjee, A Slantlet transform based intelligent system for magnetic resonance brain image classification. Biomed. Signal Process. Control, 2006, 1(4): 299-306.
A. Ortiz, J.M. Górriz, J. Ramírez, et al., Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Appl. Soft Comput., 2013, 13(5): 2668-2682.
Z. Kapás, L. Lefkovits, and L. Szilágyi, Automatic detection and segmentation of brain tumor using random forest approach. Modeling decisions for artificial intelligence, 2016: 301-312.
S. Abdullah, Round randomized learning vector quantization for brain tumor imaging. Comput. Math. Methods Med., 2016.
H. Dong, G. Yang, F. Liu, et al., Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. Medical image understanding and analysis, 2017: 506-517.
M. Havaei, Brain tumor segmentation with deep neural networks. Med. Image Anal., 2017, 35: 18-31.
A. Anand, Brain tumor segmentation using watershed technique and self organizing maps. Indian J. Sci. Technol., 2017, 10(44).
M. Ganesh, M. Naresh, and C. Arvind, MRI brain image segmentation using enhanced adaptive fuzzy K-means algorithm. Intell. Autom. Soft Comput., 2017, 23(2): 325-330.
D. Yamamoto, Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc., 2010, 34(5): 404-413.
J.C. Fu, C.C. Chen, J.W. Chai, et al., Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging. Comput. Med. Imaging Graph., 2010, 34(4): 308-320.
R. Ramasamy, P. Anandhakumar, Brain tissue classification of MR images using fast fourier transform based expectation-maximization Gaussian mixture model. Advances in computing and information technology. 2011: 387-398.
H. Ali, M. Elmogy, E. El-Daydamony, et al., Multi-resolution MRI brain image segmentation based on morphological pyramid and fuzzy C-mean clustering. Arab. J. Sci. Eng., 2015, 40(11): 3173-3185.
B. Tanoori, Z. Azimifar, A. Shakibafar, et al., Brain volumetry: An active contour model-based segmentation followed by SVM-based classification. Comput. Biol. Med., 2011, 41(8): 619-632.
A. Demirhan, İ. Güler, Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Eng. Appl. Artif. Intell., 2011, 24(2): 358-367.
A. Kharrat, K. Gasmi, M.B. Messaoud, et al., A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo J. Sci., 2010, 17(1): 71-82.
J. Kuwazuru, Automated detection of multiple sclerosis candidate regions in MR images: False-positive removal with use of an ANN-controlled level-set method. Radiol. Phys. Technol., 2012, 5(1): 105-113.
M. Sharma, S. Mukharjee, Brain tumor segmentation using hybrid genetic algorithm and artificial neural network fuzzy inference system (anfis). Int. J. Fuzzy Log. Syst., 2012, 2(4): 31-42.
Z. Ji, Q. Sun, Y. Xia, et al., Generalized rough fuzzy c-means algorithm for brain MR image segmentation. Comput. Methods Programs Biomed., 2012, 108(2): 644-655.
K. Gasmi, A. Kharrat, M.B. Messaoud, et al., Automated segmentation of brain tumor using optimal texture features and support vector machine classifier. Image Analysis and Recognition. 2012: 230-239.
M.F. Zarandi, M. Zarinbal, and M. Izadi, Systematic image processing for diagnosing brain tumors: A Type-Ⅱ fuzzy expert system approach. Appl. Soft Comput., 2011, 11(1): 285-294.
N.V. Shree, T.N.R. Kumar, Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform., 2018, 5(1): 23-30.
S.B. Gaikwad, M.S. Joshi, Brain tumor classification using principal component analysis and probabilistic neural network. Int. J. Comput. Appl., 2015, 120(3).
N. Zhang, S. Ruan, S. Lebonvallet, et al., Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Underst., 2011, 115(2): 256-269.
X. Zhao, Y. Wu, G. Song, et al., A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal., 2018, 43: 98-111.
B.M. Zahran, Classification of brain tumor using neural network. Comput. Softw., 2014: 673.
G.Z. Li, J. Yang, C.Z. Ye, et al., Degree prediction of malignancy in brain glioma using support vector machines. Comput. Biol. Med., 2006, 36(3): 313-325.
H. Mohsen, E. -S.A. El-Dahshan, E.S.M. El-Horbaty, et al., Intelligent methodology for brain tumors classification in magnetic resonance images. Int. J. Comput., 2017.
G. Vishnuvarthanan, M.P. Rajasekaran, P. Subbaraj, et al., An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl. Soft Comput., 2016, 38: 190-212.
B.H. Menze, The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging, 2015, 34(10): 1993-2024.
E. -S.A. El-Dahshan, T. Hosny, and A. -B.M. Salem, Hybrid intelligent techniques for MRI brain images classification. Digit. Signal Process., 2010, 20(2): 433-441.
D.S. Nachimuthu, A. Baladhandapani, Multidimensional texture characterization: On analysis for brain tumor tissues using MRS and MRI. J. Digit. Imaging, 2014, 27(4): 496-506.
D. Aju, R. Rajkumar, T1-T2 weighted MR image composition and cataloguing of brain tumor using regularized logistic regression. J. Teknol., 2016, 78(9): 149-159.
N. Marshkole, B.K. Singh, and A.S. Thoke, Texture and shape based classification of brain tumors using linear vector quantization. Int. J. Comput. Appl., 2011, 30(11): 21-23.
A.G. van der Kolk, J. Hendrikse, J.J. Zwanenburg, et al., Clinical applications of 7 T MRI in the brain. Eur. J. Radiol., 2013, 82(5): 708-718.
J. Ker, L. Wang, J. Rao, et al., Deep learning applications in medical image analysis. IEEE Access, 2018, 6: 9375-9389.
P. Mlynarski, H. Delingette, A. Criminisi, et al., 3D convolutional neural networks for tumor segmentation using long-range 2D context. Comput. Med. Imaging Graph, 2019.
F.G. Zöllner, K.E. Emblem, and L.R. Schad, SVM-based glioma grading: Optimization by feature reduction analysis. Z. Für Med. Phys., 2012, 22(3): 205-214.
J. Sachdeva, V. Kumar, I. Gupta, et al., Segmentation, feature extraction, and multiclass brain tumor classification. J. Digit. Imaging, 2013, 26(6): 1141-1150.
J. Jiang, Y. Wu, M. Huang, et al., 3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets. Comput. Med. Imaging Graph., 2013, 37(7): 512-521.
C. Ma, G. Luo, and K. Wang, Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Trans. Med. Imaging, 2018, 37(8): 1943-1954.
K.Y. Lim, R. Mandava, A multi-phase semi-automatic approach for multisequence brain tumor image segmentation. Expert Syst. Appl., 2018, 112: 288-300.
M. Prastawa, E. Bullitt, and G. Gerig, Simulation of brain tumors in MR images for evaluation of segmentation efficacy. Med. Image Anal., 2009, 13(2): 297-311.
N. Porz, Multi-modal glioblastoma segmentation: man versus machine. PloS One, 2014, 9(5): e96873.
A. Gooya, GLISTR: Glioma image segmentation and registration. IEEE Trans. Med. Imaging, 2012, 31(10): 1941-1954.
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.