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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Review Article

Advancements of MRI-based Brain Tumor Segmentation from Traditional to Recent Trends: A Review

Author(s): Thiyagarajan Padmapriya, Padmanaban Sriramakrishnan, Thiruvenkadam Kalaiselvi* and Karuppanagounder Somasundaram

Volume 18, Issue 12, 2022

Published on: 18 April, 2022

Article ID: e151221198933 Pages: 15

DOI: 10.2174/1573405617666211215111937

Price: $65

Abstract

Background: Among brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is challenging due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis, and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF).

Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques to deep learning through machine learning on MRI of human head scans.

Methods: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed.

Results: The primary aim of the paper is to motivate young researchers towards the development of efficient brain tumor segmentation techniques using conventional as well as recent technologies.

Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mainly applied for brain tumor detection, whereas deep learning methods were good at segmenting tumor substructures.

Keywords: Brain tumor, magnetic resonance imaging, tumor detection, tumor segmentation, image processing, machine learning, deep learning, BraTS dataset, graphics processing units.

Graphical Abstract

[1]
Prince JL, Links JM. Medical Imaging Signals and Systems. Upper Saddle River: Pearson Prentice Hall 2006.
[2]
Dhage P, Phegade MR, Shah SK. Watershed segmentation brain tumor detection. 2015 International Conference on Pervasive Computing (ICPC). Pune, India. 2015 January 8; 1-5.
[3]
Lin W. Principles of magnetic resonance imaging: A signal processing perspective. IEEE Eng Med Biol Mag 2000; 19(5): 129-30.
[http://dx.doi.org/10.1109/MEMB.2000.870245]
[4]
Kalaiselvi T. Brain Portion Extraction and Brain Abnormality Detection from Magnetic Resonance Imaging of Human Head Scans. South India: Pallavi Publication 2011.
[5]
Drevelegas A, Papanikolaou N. Imaging modalities in brain tumors.Imaging of Brain Tumors with Histological Correlations. Berlin, Heidelberg: Springer 2011; pp. 13-33.
[http://dx.doi.org/10.1007/978-3-540-87650-2_2]
[6]
Sriramakrishnan P, Kalaiselvi T, Rajeswaran R. Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybern Biomed Eng 2019; 39(2): 470-87.
[http://dx.doi.org/10.1016/j.bbe.2019.02.002]
[7]
Donoso R, Veloz A, Allende H. Modified expectation maximization algorithm for MRI segmentation. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Berlin, Heidelberg: Springer 2010; pp. 63-70.
[http://dx.doi.org/10.1007/978-3-642-16687-7_13]
[8]
Pedoia V, Balbi S, Binaghi E. Fully automatic brain tumor segmentation by using competitive EM and graph cut. In: Murino V, Puppo E, Eds. Image Analysis and Processing - ICIAP 2015. 2015 Sep 7; Cham: Springer 2015; pp. In: ICIAP 2015; 568-78.
[http://dx.doi.org/10.1007/978-3-319-23231-7_51]
[9]
Yousefi S, Azmi R, Zahedi M. Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms. Med Image Anal 2012; 16(4): 840-8.
[http://dx.doi.org/10.1016/j.media.2012.01.001] [PMID: 22377656]
[10]
Merisaari H, Parkkola R, Alhoniemi E, et al. Gaussian mixture model-based segmentation of MR images taken from premature infant brains. J Neurosci Methods 2009; 182(1): 110-22.
[http://dx.doi.org/10.1016/j.jneumeth.2009.05.026] [PMID: 19523488]
[11]
Somasundaram K, Kalaiselvi T. Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images. Comput Biol Med 2010; 40(10): 811-22.
[http://dx.doi.org/10.1016/j.compbiomed.2010.08.004] [PMID: 20832783]
[12]
Aslam A, Khan E, Beg MS. Improved edge detection algorithm for brain tumor segmentation. Procedia Comput Sci 2015; 58: 430-7.
[http://dx.doi.org/10.1016/j.procs.2015.08.057]
[13]
Anithadevi D, Perumal K. Rough set and multi-thresholds based seeded region growing algorithm for image segmentation.Artificial Intelligence and Evolutionary Computations in Engineering Systems Advances in Intelligent Systems and Computing. Singapore: Springer 2018; pp. 369-79.
[14]
Rajendran A, Dhanasekaran RJ. Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: A combined approach. Procedia Eng 2012; 30: 327-33.
[http://dx.doi.org/10.1016/j.proeng.2012.01.868]
[15]
Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK. A novel content-based active contour model for brain tumor segmentation. Magn Reson Imaging 2012; 30(5): 694-715.
[http://dx.doi.org/10.1016/j.mri.2012.01.006] [PMID: 22459443]
[16]
Tanoori B, Azimifar Z, Shakibafar A, Katebi S. Brain volumetry: An active contour model-based segmentation followed by SVM-based classification. Comput Biol Med 2011; 41(8): 619-32.
[http://dx.doi.org/10.1016/j.compbiomed.2011.05.013] [PMID: 21679935]
[17]
Zabir I, Paul S, Rayhan MA, Sarker T, Fattah SA, Shahnaz C. Automatic brain tumor detection and segmentation from multi-modal MRI images based on region growing and level set evolution. 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). 2015 December 19-20; Dhaka, Bangladesh. 503-6.
[http://dx.doi.org/10.1109/WIECON-ECE.2015.7443979]
[18]
Zanaty EA, Ghoniemy S. Medical image segmentation techniques: An overview. JIMDP 2016; 1(1): 16-37.
[19]
Pei L, Reza SM, Li W, Davatzikos C, Iftekharuddin KM. Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI. Proc SPIE Int Soc Opt Eng. 2017; 10134: p. 101342L.
[20]
Shubhangi DC, Hiremath PS. Support Vector Machine (SVM) classifier for brain tumor detection. ICAC3 '09: International Conference on Advances in Computing, Communication and Control. 2009 Jan 23-24; Mumbai, India. 444-8.
[21]
Kapás Z, Lefkovits L, Szilágyi L. Automatic detection and segmentation of brain tumor using random forest approach. In: Torra V, Narukawa Y, Navarro-Arribas G, Yañez C, Eds. Modeling Decisions for Artificial Intelligence Lecture Notes in Computer Science. 2016 Sep 19; Cham Springer 9880: 301-12.
[http://dx.doi.org/10.1007/978-3-319-45656-0_25]
[22]
Havaei M, Jodoin PM, Larochelle H. Efficient interactive brain tumor segmentation as within-brain kNN classification. 22nd International Conference on Pattern Recognition. 2014 Aug 24; Stockholm, Sweden. 556-61.
[http://dx.doi.org/10.1109/ICPR.2014.106]
[23]
Sheikh Abdullah SN, Bohani FA, Nayef BH, et al. Round randomized learning vector quantization for brain tumor imaging. Comput Math Methods Med 2016; 2016: 8603609.
[http://dx.doi.org/10.1155/2016/8603609] [PMID: 27516807]
[24]
Benson CC, Deepa V, Lajish VL, Rajamani K. Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2016 Sep 21; Jaipur, India. 2016; pp. 187-92.
[http://dx.doi.org/10.1109/ICACCI.2016.7732045]
[25]
Singh B, Aggarwal P. Detection of brain tumor using modified mean-shift based fuzzy c-mean segmentation from MRI Images. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). 2017 October 3-5; Vancouver, BC, Canada. 536-45.
[http://dx.doi.org/10.1109/IEMCON.2017.8117123]
[26]
Anand A. Brain tumor segmentation using watershed technique and self organizing maps. Indian J Sci Technol 2017; 10(44): 1-6.
[http://dx.doi.org/10.17485/ijst/2017/v10i44/120574]
[27]
Ganesh M, Naresh M, Arvind C. MRI brain image segmentation using enhanced adaptive fuzzy K-means algorithm. Intell Autom Soft Comput 2017; 23(2): 325-30.
[http://dx.doi.org/10.1080/10798587.2016.1231472]
[28]
Pan Y, Huang W, Lin Z, et al. Brain tumor grading based on neural networks and convolutional neural networks. Ann Int Conf IEE Eng Med Biol Soc 2015 2015; 699-702.
[http://dx.doi.org/10.1109/EMBC.2015.7318458]
[29]
Dong H, Yang G, Liu F, Mo Y, Guo Y. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks.Medical Image Understanding and Analysis Communications in Computer and Information Science. Cham: Springer 2017; pp. 506-17.
[30]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017; 35: 18-31.
[http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID: 27310171]
[31]
Chen L, Wu Y. MRI tumor segmentation with densely connected 3D CNN. In: Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105741F (2018); 2018; Houston, Texas, United States; pp. 105741F.
[32]
Kalaiselvi T, Sriramakrishnan P, Somasundaram K. Survey of using GPU CUDA programming model in medical image analysis. Inform Med Unlocked 2017; 9: 133-44.
[http://dx.doi.org/10.1016/j.imu.2017.08.001]
[33]
Suzuki H, Toriwaki J. Automatic segmentation of head MRI images by knowledge guided thresholding. Comput Med Imaging Graph 1991; 15(4): 233-40.
[http://dx.doi.org/10.1016/0895-6111(91)90081-6] [PMID: 1913574]
[34]
Harris GJ, Barta PE, Peng LW, et al. MR volume segmentation of gray matter and white matter using manual thresholding: Dependence on image brightness. AJNR Am J Neuroradiol 1994; 15(2): 225-30.
[PMID: 8192065]
[35]
Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell 1994; 16(6): 641-7.
[http://dx.doi.org/10.1109/34.295913]
[36]
Kaur T, Saini BS, Gupta S. Optimized multi threshold brain tumor image segmentation using two dimensional minimum cross entropy based on co-occurrence matrix. In: Dey N, Bhateja V, Hassanien AE, Eds. Medical Imaging in Clinical Applications. Cham: Springer 2016; pp. 461-86.
[37]
Wong KP. Medical image segmentation: Methods and applications in functional imaging InHandbook of biomedical image analysis. In: Suri JS, Wilson DL, Laxminarayan S, Eds. Handbook of Biomedical Image Analysis. Topics in Biomedical Engineering International Book Series. Boston, MA: Springer, 2005; pp. 111-82.
[38]
Mittelhaeusser G, Kruggel F. Fast segmentation of brain magnetic resonance tomograms. In: International Conference on Computer Vision, Virtual Reality, and Robotics in Medicine; 1995 April 3; Berlin, Heidelberg: Springer; pp. 237-41.
[39]
Kaus MR, Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R. Automated segmentation of MR images of brain tumors. Radiology 2001; 218(2): 586-91.
[http://dx.doi.org/10.1148/radiology.218.2.r01fe44586] [PMID: 11161183]
[40]
Chong VF, Zhou JY, Khoo JB, Huang J, Lim TK. Tongue carcinoma: Tumor volume measurement. Int J Radiat Oncol Biol Phys 2004; 59(1): 59-66.
[http://dx.doi.org/10.1016/j.ijrobp.2003.09.089]
[41]
Sato M, Lakare S, Wan M, Kaufman A, Nakajima M. A gradient magnitude based region growing algorithm for accurate segmentation. In: Proceedings 2000 International Conference on Image Processing; 2000 September 10; Vancouver, BC, Canada; pp. 448-51.
[42]
Letteboer M, Niessen W, Willems P, Dam EB, Viergever M. Interactive multi-scale watershed segmentation of tumors in MR brain images. In: Proceedings of the IMIVA Workshop of MICCAI; 2001; Utrecht, Netherlands; pp. 1-6.
[43]
Dam E, Loog M, Letteboer M. Integrating automatic and interactive brain tumor segmentation. In: Proceedings of the 17th International Conference on Pattern Recognition; 2004 August 26; Cambridge, UK; pp. 790-3.
[http://dx.doi.org/10.1109/ICPR.2004.1334647]
[44]
Cates JE, Whitaker RT, Jones GM. Case study: An evaluation of user-assisted hierarchical watershed segmentation. Med Image Anal 2005; 9(6): 566-78.
[http://dx.doi.org/10.1016/j.media.2005.04.007] [PMID: 15919233]
[45]
Ratan R, Sharma S, Sharma SK. Multiparameter segmentation and quantization of brain tumor from MRI images. Indian J Sci Technol 2009; 2(2): 11-5.
[http://dx.doi.org/10.17485/ijst/2009/v2i2.4]
[46]
Salman SD, Bahrani AA. Segmentation of tumor tissue in gray medical images using watershed transformation method. Int J Adv Comput Technol 2010; 2(4): 123-7.
[http://dx.doi.org/10.4156/ijact.vol2.issue4.13]
[47]
Bleau A, Leon LJ. Watershed-based segmentation and region merging. Comput Vis Image Underst 2000; 77(3): 317-70.
[http://dx.doi.org/10.1006/cviu.1999.0822]
[48]
Toennies K, Rak M, Engel K. Deformable part models for object detection in medical images. Biomed Eng Online 2014; 13(1) (Suppl. 1): S1.
[http://dx.doi.org/10.1186/1475-925X-13-S1-S1] [PMID: 25077691]
[49]
He L, Peng Z, Everding B, et al. A comparative study of deformable contour methods on medical image segmentation. Image Vis Comput 2008; 26(2): 141-63.
[http://dx.doi.org/10.1016/j.imavis.2007.07.010]
[50]
Liang J, McInerney T, Terzopoulos D. United snakes. Med Image Anal 2006; 10(2): 215-33.
[http://dx.doi.org/10.1016/j.media.2005.09.002] [PMID: 16311065]
[51]
Germond L, Dojat M, Taylor C, Garbay C. A cooperative framework for segmentation of MRI brain scans. Artif Intell Med 2000; 20(1): 77-93.
[http://dx.doi.org/10.1016/S0933-3657(00)00054-3] [PMID: 11185422]
[52]
Adiga PS, Chaudhuri BB. Region based techniques for segmentation of volumetric histo-pathological images. Comput Methods Programs Biomed 2000; 61(1): 23-47.
[http://dx.doi.org/10.1016/S0169-2607(99)00026-7] [PMID: 10660267]
[53]
Khotanlou H, Colliot O, Atif J, Bloch I. 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 2009; 160(10): 1457-73.
[http://dx.doi.org/10.1016/j.fss.2008.11.016]
[54]
Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: A level set approach. IEEE Trans Pattern Anal Mach Intell 1995; 17(2): 158-75.
[http://dx.doi.org/10.1109/34.368173]
[55]
Bishop CM. Pattern Recognition and Machine Learning. 2006.
[56]
Fisher DH, Pazzani MJ, Langley P, Eds. Concept Formation: Knowledge and Experience in Unsupervised Learning. 1st Ed. Burlington, Massachusetts: Morgan Kaufmann 1991.
[57]
Mitchell TM. The discipline of machine learning. Pittsburgh: Carnegie Mellon University, School of Computer Science, Machine Learning Department 2006.
[58]
Duda RO, Hart PE, Stork DG. Pattern classification, ed. W. Interscience. 2001.
[59]
Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 1992; 3(5): 672-82.
[http://dx.doi.org/10.1109/72.159057] [PMID: 18276467]
[60]
Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS. Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imaging 1998; 17(2): 187-201.
[http://dx.doi.org/10.1109/42.700731] [PMID: 9688151]
[61]
Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 2001; 21(1-3): 43-63.
[http://dx.doi.org/10.1016/S0933-3657(00)00073-7] [PMID: 11154873]
[62]
Lin GC, Wang WJ, Kang CC, Wang CM. Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing. Magn Reson Imaging 2012; 30(2): 230-46.
[http://dx.doi.org/10.1016/j.mri.2011.09.008] [PMID: 22133286]
[63]
Szilagyi L, Benyo Z, Szilágyi SM, Adam HS. MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat No 03CH37439); 2003 September 17; Cancun, Mexico; pp. 1: 724-6.
[http://dx.doi.org/10.1109/IEMBS.2003.1279866]
[64]
Cai W, Chen S, Zhang D. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 2007; 40(3): 825-38.
[http://dx.doi.org/10.1016/j.patcog.2006.07.011]
[65]
Szilágyi L, Szilágyi SM, Benyó Z. A modified fuzzy c-means algorithm for MR brain image segmentation. International Conference Image Analysis and Recognition. 2007 Aug 22; Berlin, Heidelberg: Springer 866-77.
[http://dx.doi.org/10.1007/978-3-540-74260-9_77]
[66]
Forouzanfar M, Forghani N, Teshnehlab M. Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation. Eng Appl Artif Intell 2010; 23(2): 160-8.
[http://dx.doi.org/10.1016/j.engappai.2009.10.002]
[67]
Maintz JB, Viergever MA. A survey of medical image registration. Med Image Anal 1998; 2(1): 1-36.
[http://dx.doi.org/10.1016/S1361-8415(01)80026-8] [PMID: 10638851]
[68]
Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Villemure JG, Thiran JP. Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans Med Imaging 2004; 23(10): 1301-14.
[http://dx.doi.org/10.1109/TMI.2004.834618] [PMID: 15493697]
[69]
Vapnik V. The Nature of Statistical Learning Theory. Springer science & business media. Berlin, Germany: Springer Science & Business Media 2013.
[70]
Zhou J, Chan KL, Chong VF, Krishnan SM. Extraction of brain tumor from MR images using one-class support vector machine. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference; 2006 January 17; Shanghai, China; pp. 6411-4.
[http://dx.doi.org/10.1109/IEMBS.2005.1615965]
[71]
Cai H, Verma R, Ou Y, Lee SK, Melhem ER, Davatzikos C. Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2007 Apr 12; Arlington, VA, USA; pp. 600-3.
[http://dx.doi.org/10.1109/ISBI.2007.356923]
[72]
Verma R, Zacharaki EI, Ou Y, et al. Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images. Acad Radiol 2008; 15(8): 966-77.
[http://dx.doi.org/10.1016/j.acra.2008.01.029] [PMID: 18620117]
[73]
Ruan S, Lebonvallet S, Merabet A, Constans JM. Tumor segmentation from a multispectral MRI images by using support vector machine classification. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2007 April 12; Arlington, VA, USA; pp.1236-9.
[http://dx.doi.org/10.1109/ISBI.2007.357082]
[74]
Ruan S, Zhang N, Liao Q, Zhu Y. Image fusion for following-up brain tumor evolution. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2011 March 30 - 2011 April 2; Chicago, IL, USA; pp. 281-4.
[http://dx.doi.org/10.1109/ISBI.2011.5872406]
[75]
Zhang N, Ruan S, Lebonvallet S, Liao Q, Zhu Y. Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence. In: 2009 16th IEEE International Conference on Image Processing (ICIP); 2009 November 7; Cairo, Egypt; pp. 373-6.
[http://dx.doi.org/10.1109/ICIP.2009.5413878]
[76]
Raja PS. Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach. Biocybern Biomed Eng 2020; 40(1): 440-53.
[http://dx.doi.org/10.1016/j.bbe.2020.01.006]
[77]
Rehman ZU, Zia MS, Bojja GR, Yaqub M, Jinchao F, Arshid K. Texture based localization of a brain tumor from MR-images by using a machine learning approach. Med Hypotheses 2020; 141: 109705.
[http://dx.doi.org/10.1016/j.mehy.2020.109705] [PMID: 32289646]
[78]
Usman K, Rajpoot K. Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal Appl 2017; 20(3): 871-81.
[http://dx.doi.org/10.1007/s10044-017-0597-8]
[79]
Soltaninejad M, Yang G, Lambrou T, et al. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J CARS 2017; 12(2): 183-203.
[http://dx.doi.org/10.1007/s11548-016-1483-3] [PMID: 27651330]
[80]
Amiri S, Rekik I, Mahjoub MA. Deep random forest-based learning transfer to SVM for brain tumor segmentation. In: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP); 2016 March 21; Monastir, Tunisia; pp. 297-302.
[http://dx.doi.org/10.1109/ATSIP.2016.7523095]
[81]
Rao BS, Reddy ES. An Efficient Anti-noise Fast FCM clustering for glioblastoma multiforme tumor segmentation. Int J Comput Sci Inf Secur 2016; 14(4): 126.
[82]
Szilagyi L, Lefkovits L, Benyo B. Automatic brain tumor segmentation in multispectral MRI volumes using a fuzzy c-means cascade algorithm. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD); 2015 August 15; Zhangjiajie, China; pp. 285-91.
[http://dx.doi.org/10.1109/FSKD.2015.7381955]
[83]
Tustison NJ, Shrinidhi KL, Wintermark M, et al. Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 2015; 13(2): 209-25.
[http://dx.doi.org/10.1007/s12021-014-9245-2] [PMID: 25433513]
[84]
Wu W, Chen AY, Zhao L, Corso JJ. Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int J CARS 2014; 9(2): 241-53.
[http://dx.doi.org/10.1007/s11548-013-0922-7] [PMID: 23860630]
[85]
Ciresan D, Giusti A, Gambardella L, Schmidhuber J. Deep neural networks segment neuronal membranes in electron microscopy images. Adv Neural Inf Process Syst 2012; 25: 2843-51.
[86]
Urban G, Bendszus M, Hamprecht F, Kleesiek J. Multi-modal brain tumor segmentation using deep convolutional neural networks. In: MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, winning contribution; 2014 Sep 14; pp. 31-5.
[87]
Zikic D, Ioannou Y, Brown M, Criminisi A. Segmentation of brain tumor tissues with convolutional neural networks. In: MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS); 2014 September; Boston, Massachusetts; pp. 36-9.
[88]
Raju K, Chiplunkar NN. A survey on techniques for cooperative CPU-GPU computing. Sustain Comput Infor 2018; 19: 72-85.
[89]
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016; 35(5): 1240-51.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[90]
Dvořák P, Menze B. Local structure prediction with convolutional neural networks for multimodal brain tumor segmentation. Medical Computer Vision: Algorithms for Big Data. Cham: Springer 2015; pp. 59-71.
[91]
Rao V, Sarabi MS, Jaiswal A. Brain tumor segmentation with deep learning. In: Proceedings of the Multimodal Brain Tumor Image Segmentation Challenge held in conjunction with MICCAI 2015 (MICCAI-BRATS 2015); 2015 August; Munich; pp. 56-59.
[92]
Bal A, Banerjee M, Sharma P, Chaki R. A multi-class image classifier for assisting in tumor detection of brain using deep convolutional neural network In: Advanced Computing and Systems for Security. Singapore: Springer 2020; pp. 93-111.
[93]
Nema S, Dudhane A, Murala S, Naidu S. RescueNet: An unpaired GAN for brain tumor segmentation. Biomed Signal Process Control 2020; 55: 101641.
[http://dx.doi.org/10.1016/j.bspc.2019.101641]
[94]
Zhou Z, He Z, Jia Y. AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images. Neurocomputing 2020; 402: 235-44.
[http://dx.doi.org/10.1016/j.neucom.2020.03.097]
[95]
Chen S, Ding C, Liu M. Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recognit 2019; 88: 90-100.
[http://dx.doi.org/10.1016/j.patcog.2018.11.009]
[96]
Mlynarski P, Delingette H, Criminisi A, Ayache N. Deep learning with mixed supervision for brain tumor segmentation. J Med Imaging (Bellingham) 2019; 6(3): 034002.
[http://dx.doi.org/10.1117/1.JMI.6.3.034002] [PMID: 31423456]
[97]
Sajid S, Hussain S, Sarwar A. Brain tumor detection and segmentation in MR images using deep learning. Arab J Sci Eng 2019; 44(11): 9249-61.
[http://dx.doi.org/10.1007/s13369-019-03967-8]
[98]
Wang Y, Li C, Zhu T, Zhang J. Multimodal brain tumor image segmentation using WRN-PPNet. Comput Med Imaging Graph 2019; 75: 56-65.
[http://dx.doi.org/10.1016/j.compmedimag.2019.04.001] [PMID: 31154088]
[99]
Hussain S, Anwar SM, Majid M. Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 2018; 282: 248-61.
[http://dx.doi.org/10.1016/j.neucom.2017.12.032]
[100]
Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 2018; 43: 98-111.
[http://dx.doi.org/10.1016/j.media.2017.10.002] [PMID: 29040911]
[101]
Kalaiselvi T, Kumarashankar P, Sriramakrishnan P. Three-phase automatic brain tumor diagnosis system using patches based updated run length region growing technique. J Digit Imaging 2020; 33(2): 465-79.
[http://dx.doi.org/10.1007/s10278-019-00276-2] [PMID: 31529237]
[102]
Kalaiselvi T, Sriramakrishnan P. Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine. Int J Imaging Syst Technol 2018; 28(3): 163-74.
[http://dx.doi.org/10.1002/ima.22267]
[103]
Hameurlaine M, Moussaoui A. Survey of brain tumor segmentation techniques on magnetic resonance imaging. Nano Biomed Eng 2019; 11(2): 178-91.
[http://dx.doi.org/10.5101/nbe.v11i2.p178-191]
[104]
Ortiz A, Górriz JM, Ramírez J, Salas-Gonzalez D, Llamas-Elvira JM. Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies. Appl Soft Comput 2013; 13(5): 2668-82.
[http://dx.doi.org/10.1016/j.asoc.2012.11.020]
[105]
Kalaiselvi T, Sriramakrishnan P, Nagaraja P. Brain tumor boundary detection by edge indication map using Bi-Modal fuzzy histogram thresholding technique from MRI T2-weighted scans. International Journal of Image. IJIGSP 2016; 8(9): 51.
[http://dx.doi.org/10.5815/ijigsp.2016.09.07]
[106]
Mustaqeem A, Javed A, Fatima T. An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. IJIGSP 2012; 4(10): 34.
[http://dx.doi.org/10.5815/ijigsp.2012.10.05]
[107]
Kalaiselvi T, Somasundaram K. Fuzzy c-means technique with histogram based centroid initialization for brain tissue segmentation in MRI of head scans. In: 2011 International Symposium on Humanities, Science and Engineering Research; 2011 June 6-7; Kuala Lumpur Malaysia; pp. 149-54.
[http://dx.doi.org/10.1109/SHUSER.2011.6008489]
[108]
Kaur J, Mahajan M. Hybrid of fuzzy logic and random walker method for medical image segmentation. IJIGSP 2015; 2: 23-9.
[http://dx.doi.org/10.5815/ijigsp.2015.02.04]
[109]
Somasundaram K, Kalaiselvi T. Automatic detection of brain tumor from MRI scans using maxima transform. National Conference on Image Processing. vol. 1: 136-41.
[110]
Kalaiselvi T, Somasundaram K, Vijayalakshmi S. A Novel Self Initiating Brain Tumor Boundary Detection for MRI International Conference on Mathematical Modeling and Scientific Computation – ICMMSC12, CCIS 283. 464-70.
[111]
Othman Z, Haron H, Mohammed RAK. Comparison of Canny and Sobel edge detection in MRI images. Post Graduate Research Seminar. 133-6. Available from: https://engineering.utm.my/comp/wp-content/uploads/sites/2/2013/04/Comparison-of-Canny-and- Sobel-Edge-Detection-in-MRI-Images.pdf
[112]
Kalaiselvi T, Somasundaram K. A novel technique for finding the boundary between the cerebral hemispheres from MR axial head scans. In: 4th Indian International Conference on Artificial Intelligence; IICAI-2009 December 16-18; Tumkur, Karnataka, India; pp. 1486-502.
[113]
Sarkar S, Maindai A. Comparison of some classical edge detection techniques with their suitability analysis for medical images processing. Int J Comput Sci Eng 2015; 3(1): 81-7.
[114]
Mei X, Zheng Z, Wu B, Guo L. The edge detection of brain tumor. In: 2009 International Conference on Communications, Circuits and Systems; 2009 July 23-25; Milpitas, CA, USA; pp. 477-9.
[115]
Rulaningtyas R, Ain K. Edge detection for brain tumor pattern recognition In: International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering; 2009 November 23-25; Bandung, Indonesia; pp. 1-3.
[http://dx.doi.org/10.1109/ICICI-BME.2009.5417299]
[116]
Diwakar M, Patel PK, Gupta K. Cellular automata based edge-detection for brain tumor. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI); 2013 August 22-25; Mysore, India; pp. 53-9.
[http://dx.doi.org/10.1109/ICACCI.2013.6637146]
[117]
Juneja M, Sandhu PS. Performance evaluation of edge detection techniques for images in spatial domain. IJCTE 2009; 1(5): 614-21.
[http://dx.doi.org/10.7763/IJCTE.2009.V1.100]
[118]
Halder A, Chatterjee N, Kar Arindam, Pal S, Pramanik S. Edge detection: A statistical approach. In: 2011 3rd International Conference on Electronics Computer Technology; 2011 April 8; Kanyakumari, India; pp. 306-9. 2011; vol. 2: 306-9.
[119]
Zadeh LA. Fuzzy sets. Inf Control 1965; 8: 338-53.
[http://dx.doi.org/10.1016/S0019-9958(65)90241-X]
[120]
Mohammad A, Al-Azawi N. Image thresholding using histogram fuzzy approximation. Int J Comput Appl 2013; 83(9): 36-40.
[121]
Kalaiselvi T, Sriramakrishnan P, Vasanthi R. Brain tumor boundary detection from MRI brain scans using edge indication map. In: Proceedings of National Conferences on New Horizons in Computational Intelligence and Information Systems; 2015 December 17; Dindigul, India, pp. 154-5.
[122]
Abdou IA, Pratt W. Quantitative design and evaluation of enhancement / thresholding edge detectors. Proc IEEE 1979; 67(5): 753-66.
[http://dx.doi.org/10.1109/PROC.1979.11325]
[123]
Thillaikkarasi R, Saravanan S. An enhancement of deep learning algorithm for brain tumor segmentation using kernel based CNN with M-SVM. J Med Syst 2019; 43(4): 84.
[http://dx.doi.org/10.1007/s10916-019-1223-7] [PMID: 30810822]
[124]
Wang G, Li W, Ourselin S, Vercauteren T. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi A, Bakas S, Kuijf H, Menze B, Reyes M, Eds. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Cham: Springer, Cham, 2018; pp. 178-90.
[125]
Liu J, Li M, Wang J, Wu F, Liu T, Pan Y. A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci Technol 2014; 19(6): 578-95.
[http://dx.doi.org/10.1109/TST.2014.6961028]
[126]
Arakeri MP, Reddy GRM. Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images. Signal Image Video Process 2015; 9(2): 409-25.
[http://dx.doi.org/10.1007/s11760-013-0456-z]
[127]
Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF. Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP J Image Video Process 2018; 1: 97.
[http://dx.doi.org/10.1186/s13640-018-0332-4]
[128]
Sivakumar P, Ganeshkumar P. CANFIS based glioma brain tumor classification and retrieval system for tumor diagnosis. Int J Imaging Syst Technol 2017; 27(2): 109-17.
[http://dx.doi.org/10.1002/ima.22215]
[129]
Nayak DR, Dash R, Majhi B. Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 2016; 177: 188-97.
[http://dx.doi.org/10.1016/j.neucom.2015.11.034]
[130]
Qurat-Ul-Ain GL, Kazmi SB, Jaffar MA, Mirza AM. Classification and segmentation of brain tumor using texture analysis. In: AIKED'10: Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases Recent advances in artificial intelligence, knowledge engineering and data bases; 2010 February 20; pp. 147-55.
[131]
Chugh S, Anand SM. Semi-automated tumor segmentation from mri images using local statistics based adaptive region growing. IJIEE 2012; 2(1): 7-11.
[http://dx.doi.org/10.7763/IJIEE.2012.V2.47]
[132]
Roffo G, Melzi S, Cristani M. Infinite feature selection. In: 2015 IEEE International Conference on Computer Vision (ICCV); 2015 December 7-13; Santiago, Chile; pp. 4202-10.
[133]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[134]
Gonzalez RC. Digital Image Processing. Boston: Addison–Wesely Publishing Company 1992.
[135]
Rosen GD, Harry JD. Brain volume estimation from serial section measurements: A comparison of methodologies. J Neurosci Methods 1990; 35(2): 115-24.
[http://dx.doi.org/10.1016/0165-0270(90)90101-K] [PMID: 2283883]
[136]
Dice LR. Measures of the amount of ecologic association between species. Ecology 1945; 26(3): 297-302.
[http://dx.doi.org/10.2307/1932409]
[137]
Bauer S, Tessier J, Krieter O, Nolte LP, Reyes M. Integrated spatio-temporal segmentation of longitudinal brain tumor imaging studies. In: Menze B, Langs G, Montillo A, Kelm M, Müller H, Tu Z, Eds. Medical Computer Vision. Large Data in Medical Imaging. Lecture Notes in Computer Science, vol. 8331. Cham: Springer, 2013, pp.74-83.
[138]
Buendia P, Taylor T, Ryan M, John N. A grouping artificial immune network for segmentation of tumor images. In: MICCAI Challenge on Multimodal Brain Tumor Segmentation, 2013 September 22, Nagoya, Japan; pp. 1-5.
[139]
Cordier N, Menze B, Delingette H, Ayache N. Patch based segmentation of brain tissues. In: MICCAI Challenge on Multimodal Brain Tumor Segmentation, 2013 September 22, Nagoya, Japan; pp. 6-17.
[140]
Demirhan A, Törü M, Güler I. Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J Biomed Health Inform 2015; 19(4): 1451-8.
[http://dx.doi.org/10.1109/JBHI.2014.2360515] [PMID: 25265636]
[141]
Doyle S, Vasseur F, Dojat M, Forbes F. Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM. In: NCI-MICCAI Challenge on Multimodal Brain Tumor Segmentation. Proceedings of NCI-MICCAI BRATS 2013; 2013 September 22; Nagoya, Japan; pp. 18-22.
[142]
Pereira S, Festa J, Mariz JA, Sousa N, Silva CA. Automatic brain tissue segmentation of multi-sequence MR images using random decision forests. In: Proceedings of the MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS’13); 2013 January 1.
[143]
Geremia E, Menze B H, Ayache N. Spatial decision forests for glioma segmentation in multi-channel MR images. In: MICCAI Challenge on Multimodal Brain Tumor Segmentation; 2012; Nice, France.
[144]
Guo X, Schwartz L, Zhao B. Semi-automatic segmentation of multimodal brain tumor using active contours. In: NCI-MICCAI Challenge on Multimodal Brain Tumor Segmentation. Proceedings of NCI-MICCAI BRATS 2013; 2013 September 22; Nagoya, Japan; pp. 27-30.
[145]
Hamamci A, Kucuk N, Karaman K, Engin K, Unal G. Tumor-Cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans Med Imaging 2012; 31(3): 790-804.
[http://dx.doi.org/10.1109/TMI.2011.2181857] [PMID: 22207638]
[146]
Meier R, Bauer S, Slotboom J, Wiest R, Reyes M. Appearance-and context-sensitive features for brain tumor segmentation. In: Proceedings of MICCAI BRATS Challenge; 2014.
[147]
Reza S, Iftekharuddin KM. Multi-class abnormal brain tissue segmentation using texture. In: Multimodal Brain Tumor Segmentation; 2013 September 22; Nagoya, Japan. pp. 38.
[148]
Raviv TR, Leemput KV, Menze BH. Multi-modal brain tumor segmentation via latent atlases. Proceeding MICCAI-BRATS. 64-73.
[http://dx.doi.org/10.1/1.295.9177]
[149]
Shin HC. Hybrid clustering and logistic regression for multi-modal brain tumor segmentation. Proc of Workshops and Challanges in Medical Image Computing and Computer- Assisted Intervention. In: MICCAI’12; 2012. Available from: https://www2.imm.dtu.dk/projects/BRATS2012/ShinBRATS2012.pdf
[150]
Subbanna NK, Precup D, Collins DL, Arbel T. Hierarchical probabilistic Gabor and MRF segmentation of brain tumours in MRI volumes. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N, Eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Berlin, Heidelberg: Springer, 2013.
[http://dx.doi.org/10.1007/978-3-642-40811-3_94]
[151]
Taylor T, John N, Buendia P, Ryan M. Map-reduce enabled hidden Markov models for high throughput multimodal brain tumor segmentation. In: NCI-MICCAI Challenge on Multimodal Brain Tumor Segmentation. Proceedings of NCI-MICCAI BRATS 2013; 2013 September 22; Nagoya, Japan; pp. 43.
[152]
Tustison NJ, Johnson HJ, Rohlfing T, et al. Instrumentation bias in the use and evaluation of scientific software: Recommendations for reproducible practices in the computational sciences. Front Neurosci 2013; 7: 162.
[http://dx.doi.org/10.3389/fnins.2013.00162]
[153]
Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J. Decision forests for tissue-specific segmentation of high- grade gliomas in multi-channel MR. In: Ayache N, Delingette H, Golland P, Mori K, Eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. Lecture Notes in Computer Science, vol. 7512. Berlin, Heidelberg: Springer; 2012.
[http://dx.doi.org/10.1007/978-3-642-33454-2_46]
[154]
Sriramakrishnan P, Kalaiselvi T, Rajeswaran R. Modified local ternary patterns technique for brain tumor segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybern Biomed Eng 2019; 39(2): 470-87.
[http://dx.doi.org/10.1016/j.bbe.2019.02.002]
[155]
Kalaiselvi T, Kumarashankar P, Sriramakrishnan P. Reliability of segmenting brain tumor and finding optimal volume estimator for MR images of patients with glioma’s. Int J Innov Technol Explor Eng 2019; 8(9): 1647-52.
[http://dx.doi.org/10.35940/ijitee.I8509.078919]
[156]
Kalaiselvi T, Padmapriya ST, Sriramakrishnan P, Somasundaram K. Deriving tumor detection models using convolutional neural networks from MRI of human brain scans. Int J Inf Technol 2020; 7: 1-6.
[http://dx.doi.org/10.1007/s41870-020-00438-4]
[157]
Kalaiselvi T, Padmapriya T, Sriramakrishnan P, Priyadharshini V. Development of automatic glioma brain tumor detection system using deep convolutional neural networks. Int J Imaging Syst Technol 2020; 30(4): 926-38.
[http://dx.doi.org/10.1002/ima.22433]
[158]
Kalaiselvi T, Karthigai Selvi S. Investigation of image processing techniques in MRI based medical image analysis methods and validation metrics for brain tumor. Curr Med Imaging 2018; 14(4): 489-505.
[http://dx.doi.org/10.2174/1573405613666170614082314]
[159]
Angulakshmi M, Deepa M. A review on deep learning architecture and methods for MRI brain tumour segmentation. Curr Med Imaging 2021; 17(6): 695-706.
[160]
Ahmad HM, Khan MJ, Yousaf A, Ghuffar S, Khurshid K. Deep Learning: A breakthrough in medical imaging. Curr Med Imaging Rev 2020; 16(8): 946-56.
[http://dx.doi.org/10.2174/1573405615666191219100824] [PMID: 33081657]

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