An analytical research study of MRI brain tumor modalities and classification techniques

 
 
 
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  • Keywords
  • References
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  • Abstract


    In MRI image analysis, brain cancer or tumor analysis is the challenging task for the doctors due to the complex structure of the human brain and high assortment in the appearance of cancerous tissues. At present brain tumor detection and its diagnosis is very essential to reduce the death rate of brain cancer patients. The brain tumor recognition process can be performed by various standard image processing techniques for e.g. MRI (magnetic resonance imaging), ECG (Electro-Encephalography) and many more. Among these, MRI imaging is the emerging tumor detection technique. The efficiency of the tumor detection process provides anatomical knowledge about cancerous tissues in the MRI brain, which helps the doctors for tumor diagnosis. The comprehensive survey study provides different MRI brain tumor detection and classification techniques based on WHO grade system report and different imaging modalities. The classification taxonomy is presented based on segmentation and feature extraction methods. Based on the prior research study, have mainly focused on different MRI imaging modality and evaluated performance and classification accuracy. The last section of the survey study mainly highlighting research challenges which could help for future research in MRI brain tumor detection and classification techniques.

     


  • Keywords


    Brain Tumor; Classification; Feature Extraction; MRI Image Modalities; SVM; Segmentation; Tumor Detection.

  • References


      [1] Singh N, Jindal A (2012) Ultra-sonogram images for thyroid segmentation and texture classification in the diagnosis of malignant (cancerous) or benign (noncancerous) nodules. Int J Eng Innov Technol 1(5):202–206.

      [2] Christ MCJ, Sivagowri S, Babu PG (2014) Segmentation of brain tumors using metaheuristic algorithms. Open J Commun Softw 1(1):1–10. https://doi.org/10.15764/CS.2014.01001.

      [3] Charfi S, Lahmyed R, Rangarajan L (2014) A novel approach for brain tumor detection using neural network. Int J Res Eng Technol 2(7):93–104.

      [4] Logeswari T, Karnan M (2010) An improved implementation of brain tumor detection using segmentation based on hierarchical self-organizing map. Int J Comput Theory Eng 2(4):1793–8201. https://doi.org/10.7763/IJCTE.2010.V2.207.

      [5] Yang G, Raschke F, Barrick TR, Howe FA (2015) Manifold learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering. Magn Reson Med 74(3):868–878 https://doi.org/10.1002/mrm.25447.

      [6] Yang G, Raschke F, Barrick TR, Howe FA (2014) Classification of a brain tumor 1 H MR spectra: extracting features by metabolite quantification or nonlinear manifold learning? In: Proceedings of IEEE 11th international symposium on biomedical imaging (ISBI), Beijing, China https://doi.org/10.1109/ISBI.2014.6868051.

      [7] Yang G, Nawaz T, Barrick TR, Howe FA, Slabaugh G (2015) Discrete wavelet transform-based whole-spectral and subspectral analysis for improved brain tumor clustering using single voxel MR spectroscopy. IEEE Trans Biomed Eng 62(12):2860–2866 https://doi.org/10.1109/TBME.2015.2448232.

      [8] Jones TL, Byrnes TJ, Yang G, Howe FA, Anthony B, Barrick TR (2014) Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique. Neuro-oncology 17(3):466–476 https://doi.org/10.1093/neuonc/nou159.

      [9] Yang G, Jones TL, Barrick TR, Howe FA (2014) Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p: q tensor decomposition of diffusion tensor imaging. NMR Biomed 27(9):1103–1111 https://doi.org/10.1002/nbm.3163.

      [10] Yang G, Jones TL, Howe FA, Barrick TR (2016) Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three-dimensional shape analysis. Magn Reson Med 75(6):2505–2516. https://doi.org/10.1002/mrm.25845.

      [11] What you need to know about tm brain tumors (2009) Patient Education Publications, National Cancer Institute. https ://www.cance r.gov/publicatio ns/patie nt-education.

      [12] Kleihues P, Burger PC, Scheithauer BW (2013) The new WHO classification of brain tumors. Brain Pathol 3(3):255–268. https://doi.org/10.1111/j.1750-3639.1993.tb00752.x.

      [13] Liu, Jin, Min Li, Jianxin Wang, Fangxiang Wu, Tianming Liu, and Yi Pan. "A survey of MRI-based brain tumor segmentation methods." Tsinghua Science and Technology 19, no. 6 (2014): 578-595. https://doi.org/10.1109/TST.2014.6961028.

      [14] Chang H-H, Valentino DJ, Duckwiler GR, Toga AW (2007) Segmentation of brain MR images using a charged fluid model. IEEE Trans Biomed Eng 54(10):1798–1813. https://doi.org/10.1109/TBME.2007.895104.

      [15] Chen P-F, Steen RG, Yezzi A, Krim H (2009) Brain Mri T1-map and T1-weighted image segmentation in a variational framework. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, Taipei, Taiwan, pp 417–420.

      [16] Drevelegas and N. Papanikolaou, Imaging modalities in brain tumors, in Imaging of Brain Tumors with Histological Correlations. Springer, 2011, pp. 13-33. https://doi.org/10.1007/978-3-540-87650-2_2.

      [17] 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, Neuroinformatics 13 (2015) 209–225. https://doi.org/10.1007/s12021-014-9245-2.

      [18] Guyon, Isabelle, Jason Weston, Stephen Barnhill, and Vladimir Vapnik. "Gene selection for cancer classification using support vector machines." Machine learning 46, no. 1-3 (2002): 389-422.Aaa https://doi.org/10.1023/A:1012487302797.

      [19] Khan, Javed, Jun S. Wei, Markus Ringner, Lao H. Saal, Marc Ladanyi, Frank Westermann, Frank Berthold et al. "Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks." Nature medicine 7, no. 6 (2001): 673. https://doi.org/10.1038/89044.

      [20] Clark, Matthew C., Lawrence O. Hall, Dmitry B. Goldgof, Robert Velthuizen, F. Reed Murtagh, and Martin S. Silbiger. "Automatic tumor segmentation using knowledge-based techniques." IEEE transactions on medical imaging 17, no. 2 (1998): 187-201. https://doi.org/10.1109/42.700731.

      [21] Alfonse, Marco, and Abdel-Badeeh M. Salem. "An automatic classification of brain tumors through MRI using support vector machine." Egyptian Computer Science Journal (2016).

      [22] Mahesh, K. Michael, and J. Arokia Renjit. "Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review." Evolutionary Intelligence 11, no. 1-2 (2018): 19-30. https://doi.org/10.1007/s12065-018-0156-2.

      [23] Kaushik D, Singh U, Singhal P, Singh V (2013) Medical image segmentation using genetic algorithm. Int J Comput Appl 81(18):10–15. https://doi.org/10.5120/14222-2220.

      [24] 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. https://doi.org/10.1016/j.neuroimage.2004.06.022.

      [25] Abdel-Maksoud E, Elmogy M, Al-Awadi R (2015) Brain tumor segmentation based on a hybrid clustering technique. Egypt Inf 16(1):71–81, https://doi.org/10.1016/j.eij.2015.01.003.

      [26] Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal 8(3):275–283 https://doi.org/10.1016/j.media.2004.06.007.

      [27] Bhattacharyya D, Kim TH (2011) Brain tumor detection using MRI image analysis. In: Proceedings of international conference on ubiquitous computing and multimedia applications, Berlin, Heidelberg, pp 307–314 https://doi.org/10.1007/978-3-642-20998-7_38.

      [28] Dawngliana M, Deb D, Handique M, Roy S (2015) Automatic brain tumor segmentation in MRI: hybridized multilevel thresholding and level set. In: Proceedings of international symposium on advanced computing and communication (ISACC), Silchar, India, pp 219–223. https://doi.org/10.1109/ISACC.2015.7377345.

      [29] Bhanumurthy MY, Anne K (2014) An automated detection and segmentation of tumor in brain MRI using artificial intelligence. In: Proceedings of international conference on computational intelligence and computing research (ICCIC), Coimbatore, India, pp 1–9 https://doi.org/10.1109/ICCIC.2014.7238374.

      [30] Wong KP (2005) Medical image segmentation: methods and applications in functional imaging. Handbook of biomedical image analysis. Springer, Berlin, pp 111–182. https://doi.org/10.1007/0-306-48606-7_3.

      [31] Chandra GR, Rao KRH (2016) Tumor detection in brain using genetic algorithm. Procedia Comput Sci 79:449–457 https://doi.org/10.1016/j.procs.2016.03.058.

      [32] Ilunga-Mbuyamba E, Cruz-Duarte JM, Avina-Cervantes JG, Correa-Cely CR, Lindner D, Chalopin C (2016) Active contours driven by Cuckoo search strategy for brain tumor images segmentation. Expert Syst Appl 56:59–68 https://doi.org/10.1016/j.eswa.2016.02.048.

      [33] Ladgham A, Sakly A, Mtibaa A (2014) MRI brain tumor recognition using modified shuffled frog leaping algorithm. In: Proceedings of international conference on sciences and techniques of automatic control & computer engineering, Hammamet, Tunisia, pp 504–507. https://doi.org/10.1109/STA.2014.7086694.

      [34] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, 1981 https://doi.org/10.1007/978-1-4757-0450-1.

      [35] 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. https://doi.org/10.1016/j.mri.2011.09.008.

      [36] L. Szilagyi, Z. Benyo, S. M. Szilagyi, and H. Adam, ´Mr brain image segmentation using an enhanced fuzzy cmeans 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.

      [37] 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. https://doi.org/10.24297/ijct.v5i1.4387.

      [38] Bhatia M, Bansal A, Yadav D (2017) A proposed quantitative approach to classify brain MRI. Int J Syst Assur Eng Manag 8(2):577–584 https://doi.org/10.1007/s13198-016-0465-8.

      [39] Nasir M, Baig A, Khanum A (2014) Brain tumor classification in MRI scans using sparse representation. In: Proceedings of international conference on image and signal processing, vol 8509. Springer, Cham, pp 629–637. https://doi.org/10.1007/978-3-319-07998-1_72.

      [40] El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Dig Signal Process 20(2):433–441. https://doi.org/10.1016/j.dsp.2009.07.002.

      [41] Deepa AR, Mercy WR, Emmanuel S (2016) Identification and classification of brain tumor through mixture model based on magnetic resonance imaging segmentation and artificial neural network. Arab J Sci Eng 45A(2):1–12. https://doi.org/10.1002/cmr.a.21390.

      [42] Jiang J, Trundle P, Ren J (2010) Medical image analysis with artificial neural networks. Comput Med Imaging Gr 34(8):617–631. https://doi.org/10.1016/j.compmedimag.2010.07.003.

      [43] Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A (2015) An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput J 38:190–212 https://doi.org/10.1016/j.asoc.2015.09.016.

      [44] Zhang Y, Dong Z, Wu L, Wang S (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053 https://doi.org/10.1016/j.eswa.2011.02.012.

      [45] Pereira S, Pinto A, Alves A, Silva CA (2015) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251 https://doi.org/10.1109/TMI.2016.2538465.

      [46] Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput Appl 28(3):565–574. https://doi.org/10.1007/s00521-015-2089-3.

      [47] Amsaveni V, Singh NA, Dheeba J (2014) Application of support vector machine classifier for computer aided diagnosis of brain tumor from MRI. In: Proceedings of international conference on swarm, evolutionary, and memetic computing. Springer, Cham, pp 514–522 https://doi.org/10.1007/978-3-319-20294-5_45.

      [48] Zhang N, Ruan S, Lebonvallet S, Liao Q, Zhu Y (2009) Multikernel SVM based classification for brain tumor segmentation of MRI multi-sequence. In: Proceedings of IEEE international conference on image processing, Cairo, Egypt, pp 3373–3376

      [49] Nabizadeh N, Kubat M (2015) Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comput Electr Eng 45:286–301 https://doi.org/10.1016/j.compeleceng.2015.02.007.

      [50] Kharrat A, Halima MB, Ayed MB (2015) MRI brain tumor classification using support vector machines and meta-heuristic method. In: Proceedings of international conference on intelligent systems design and applications (ISDA), Marrakech, Morocco, pp 446–451. https://doi.org/10.1109/ISDA.2015.7489271.

      [51] Kalbkhani H, Shayesteha MG, Zali-Vargahana B (2013) Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series. Biomed Signal Process Control 8(6):909–919. https://doi.org/10.1016/j.bspc.2013.09.001.

      [52] Nie J, Xue Z, Liu T, Young GS, Setayesh K, Guo L, Wong STC (2009) Automated brain tumor segmentation using spatial accuracy- weighted hidden Markov random field. Comput Med Imaging Gr 33(6):431–441. https://doi.org/10.1016/j.compmedimag.2009.04.006.

      [53] Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Villemure J-G, Thiran JP (2004) Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans Med Imaging 23(10):1301–1314 https://doi.org/10.1109/TMI.2004.834618.

      [54] Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2017) Automated brain tumor detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg 12(2):183–203. https://doi.org/10.1007/s11548-016-1483-3.

      [55] Shree, N. Varuna, and T. N. R. Kumar. "Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network." Brain informatics 5, no. 1 (2018): 23-30. https://doi.org/10.1007/s40708-017-0075-5.

      [56] Mohsen, Heba, El-Sayed A. El-Dahshan, El-Sayed M. El-Horbaty, and Abdel-Badeeh M. Salem. "Classification using deep learning neural networks for brain tumors." Future Computing and Informatics Journal 3, no. 1 (2018): 68-71. https://doi.org/10.1016/j.fcij.2017.12.001.

      [57] Dong, Hao, Guang Yang, Fangde Liu, Yuanhan Mo, and Yike Guo. "Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks." In Annual Conference on Medical Image Understanding and Analysis, pp. 506-517. Springer, Cham, 2017. https://doi.org/10.1007/978-3-319-60964-5_44.

      [58] Mathew, A. Reema, and P. Babu Anto. "Tumor detection and classification of MRI brain image using wavelet transform and SVM." In Signal Processing and Communication (ICSPC), 2017 International Conference on, pp. 75-78. IEEE, 2017. https://doi.org/10.1109/CSPC.2017.8305810.

      [59] Shil, S. K., F. P. Polly, M. A. Hossain, Md Shareef Ifthekhar, Mohammad Nasir Uddin, and Y. M. Jang. "An improved brain tumor detection and classification mechanism." In 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 54-57. IEEE, 2017. https://doi.org/10.1109/ICTC.2017.8190941.

      [60] Usman, Khalid, and Kashif Rajpoot. "Brain tumor classification from multi-modality MRI using wavelets and machine learning." Pattern Analysis and Applications 20, no. 3 (2017): 871-881. https://doi.org/10.1007/s10044-017-0597-8.

      [61] Amin, Javeria, Muhammad Sharif, Mussarat Yasmin, and Steven Lawrence Fernandes. "A distinctive approach in brain tumor detection and classification using MRI." Pattern Recognition Letters (2017). https://doi.org/10.1016/j.patrec.2017.10.036.

      [62] Cheng, Jun, Wei Huang, Shuangliang Cao, Ru Yang, Wei Yang, Zhaoqiang Yun, Zhijian Wang, and Qianjin Feng. "Enhanced performance of brain tumor classification via tumor region augmentation and partition." PloS one 10, no. 10 (2015): e0140381. https://doi.org/10.1371/journal.pone.0140381.

      [63] Alfonse, Marco, and Abdel-Badeeh M. Salem. "An automatic classification of brain tumors through MRI using support vector machine." Egyptian Computer Science Journal (2016).

      [64] Kermi, Adel, Khaled Andjouh, and Ferhat Zidane. "Fully automated brain tumor segmentation system in 3D-MRI using symmetry analysis of brain and level sets." IET Image Processing 12, no. 11 (2018): 1964-1971. https://doi.org/10.1049/iet-ipr.2017.1124.

      [65] Bahadure, Nilesh Bhaskarrao, Arun Kumar Ray, and Har Pal Thethi. "Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM." International journal of biomedical imaging 2017 (2017). https://doi.org/10.1155/2017/9749108.

      [66] Praveen, G. B., and Anita Agrawal. "Hybrid approach for brain tumor detection and classification in magnetic resonance images." In Communication, Control and Intelligent Systems (CCIS), 2015, pp. 162-166. IEEE, 2015. https://doi.org/10.1109/CCIntelS.2015.7437900.

      [67] Ilhan, Umit, and Ahmet Ilhan. "Brain tumor segmentation based on a new threshold approach." Procedia Computer Science 120 (2017): 580-587. https://doi.org/10.1016/j.procs.2017.11.282.

      [68] Kaur, Taranjit, Barjinder Singh Saini, and Savita Gupta. "Quantitative metric for MR brain tumor grade classification using sample space density measure of analytic intrinsic mode function representation." IET Image Processing 11, no. 8 (2017): 620-632. https://doi.org/10.1049/iet-ipr.2016.1103.

      [69] Matthew, A. Reema, Achala Prasad, and P. Babu Anto. "A review on feature extraction techniques for tumor detection and classification from brain MRI." In Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017 International Conference on, pp. 1766-1771. IEEE, 2017. https://doi.org/10.1109/ICICICT1.2017.8342838.

      [70] Louis, David N., Arie Perry, Guido Reifenberger, Andreas Von Deimling, Dominique Figarella-Branger, Webster K. Cavenee, Hiroko Ohgaki, Otmar D. Wiestler, Paul Kleihues, and David W. Ellison. "The 2016 World Health Organization classification of tumors of the central nervous system: a summary." Acta neuropathologica 131, no. 6 (2016): 803-820. https://doi.org/10.1007/s00401-016-1545-1.

      [71] Sauwen, Nicolas, M. Acou, S. Van Cauter, D. M. Sima, J. Veraart, Frederik Maes, Uwe Himmelreich, E. Achten, and Sabine Van Huffel. "Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI." NeuroImage: Clinical 12 (2016): 753-764. https://doi.org/10.1016/j.nicl.2016.09.021.

      [72] Sornam, M., Muthu Subash Kavitha, and R. Shalini. "Segmentation and classification of brain tumor using wavelet and Zernike based features on MRI." In Advances in Computer Applications (ICACA), IEEE International Conference on, pp. 166-169. IEEE, 2016. https://doi.org/10.1109/ICACA.2016.7887944.

      [73] Anitha, V., and S. Murugavalli. "Brain tumor classification using two-tier classifier with adaptive segmentation technique." IET computer vision 10, no. 1 (2016): 9-17. https://doi.org/10.1049/iet-cvi.2014.0193.

      [74] Abdel-Maksoud, Eman, Mohammed Elmogy, and Rashid Al-Awadi. "Brain tumor segmentation based on a hybrid clustering technique." Egyptian Informatics Journal 16, no. 1 (2015): 71-81. https://doi.org/10.1016/j.eij.2015.01.003.

      [75] Huang, Meiyan, Wei Yang, Yao Wu, Jun Jiang, Wufan Chen, and Qianjin Feng. "Brain tumor segmentation based on local independent projection-based classification." IEEE transactions on biomedical engineering 61, no. 10 (2014): 2633-2645 https://doi.org/10.1109/TBME.2014.2325410.

      [76] Murthy, Deepthi TS, G. Sadashivappa, and Ravi D. Shankar. "Novel Mechanism of Classifying the Brain Tumor for Identifying its Critical State." International Journal of Advanced Computer Science and Applications, Vol. 9(9), 2018, pp. 57-66. https://doi.org/10.14569/IJACSA.2018.090908.


 

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Article ID: 29561
 
DOI: 10.14419/ijet.v8i3.29561




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