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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

A Review on State-of-the-Art Techniques for Image Segmentation and Classification for Brain MR Images

Author(s): Aswathy S. U* and Ajith Abraham

Volume 19, Issue 3, 2023

Published on: 25 August, 2022

Article ID: e260422204046 Pages: 28

DOI: 10.2174/1573405618666220426100944

Price: $65

Abstract

The diagnosis of tumors in the initial stage plays a crucial role in improving the clinical outcomes of a patient. Evaluation of brain tumors from many MRI images generated regularly in a clinical environment is a complex and time-consuming process. Therefore,there comes a need for an efficient and accurate model for the early detection of tumors. This paper revolves around the current strategies used for brain tumor segmentation and classification from MRI images of the brain. This approach also tries to pave the way for the significance of their performance measure and quantitative evaluation of forefront strategies. This state of the art clearly describes the importance of several brain image segmentation and classification methodsduring the past 13 years of publication by various researchers. In this instance, new calculations are being made for potential clients to analyze the concerned area of research. This review acknowledges the key accomplishments expressed in the diagnostic measures and their success indicators of qualitative and quantitative measurement. This research study also explores the key outcomes and reasons for finding the lessons learned.

Keywords: MRI, segmentation, classification, brain tumor, image segmentation, CT.

Graphical Abstract

[1]
Agrawal S, Samantaray L, Panda R, Dora L. A new hybrid adaptive cuckoo search-squirrel search algorithm for brain MR image analysis. In: Bhattacharyya S, Konar D, Platos J, Kar C, Sharma K, Eds. Hybrid Machine Intelligence for Medical Image Analysis. Singapore: Springer 2020; pp. 85-117.
[http://dx.doi.org/10.1007/978-981-13-8930-6_5]
[2]
Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HFA. A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magn Reson Imaging 2019; 61: 300-18.
[http://dx.doi.org/10.1016/j.mri.2019.05.028] [PMID: 31173851]
[3]
Jayade S, Ingole DT, Ingole MD. Review of brain tumor detection concept using MRI images. In: 2019 International Conference on Innovative Trends and Advances in Engineering and Technology (ICITAET) 2019 Dec 27-28; Shegoaon, India. 206-9.
[http://dx.doi.org/10.1109/ICITAET47105.2019.9170144]
[4]
Raghtate GS, Salankar SS. Modified fuzzy C means with optimized ant colony algorithm for image segmentation. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN) 2015 Dec 12-14; Jabalpur, India. 1283-8.
[http://dx.doi.org/10.1109/CICN.2015.246]
[5]
Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Patt Recognit Lett 2020; 131: 244-60.
[http://dx.doi.org/10.1016/j.patrec.2019.11.020]
[6]
Derraz F, Beladgham M, Khelif MH. Application of active contour models in medical image segmentation. In: International Conference on Information Technology: Coding and Computing 2004 Proceedings ITCC 2004. Vol. 2: 675-81.
[http://dx.doi.org/10.1109/ITCC.2004.1286732]
[7]
Vishnuvarthanan A, Rajasekaran MP, Govindaraj V, Zhang Y, Thiyagarajan A. An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 2017; 57: 399-426.
[http://dx.doi.org/10.1016/j.asoc.2017.04.023]
[8]
Wasule V, Sonar P. Classification of brain MRI using SVM and KNN classifier. In: 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS) 2017 May 4-5; Chennai, India. 218-3.
[http://dx.doi.org/10.1109/SSPS.2017.8071594]
[9]
Wang Y, Liu L, Zhang H, Cao Z, Lu S. Image segmentation using active contours with normally biased GVF external force. IEEE Signal Process Lett 2010; 17(10): 875-8.
[http://dx.doi.org/10.1109/LSP.2010.2060482]
[10]
Meier R, Knecht U, Loosli T, et al. Clinical evaluation of a fully-automatic segmentation method for longitudinal brain tumor volumetry. Sci Rep 2016; 6(1): 23376.
[http://dx.doi.org/10.1038/srep23376] [PMID: 27001047]
[11]
Bharanidharan N, Rajaguru H, Geetha V. Performance analysis of KNN classifier with and without GLCM features in brain tumor detection. Int J Innov Technol Explor Eng 2018; 8: 103-6.
[12]
Anand A, Kaur H. Survey on segmentation of brain tumor: A review of literature. Int J Adv Res Comput Commun Eng 2016; 5(1): 79-82.
[http://dx.doi.org/10.17148/IJARCCE.2016.5118]
[13]
Nakano T, Asano K, Miura H, Itoh S, Suzuki S. Meningiomas with brain edema: Radiological characteristics on MRI and review of the literature. Clin Imaging 2002; 26(4): 243-9.
[http://dx.doi.org/10.1016/S0899-7071(02)00433-3] [PMID: 12140153]
[14]
Rees J. Advances in magnetic resonance imaging of brain tumours. Curr Opin Neurol 2003; 16(6): 643-50.
[http://dx.doi.org/10.1097/00019052-200312000-00001] [PMID: 14624071]
[15]
Aswathy SU, Devadhas G, Kumar SS. Brain tumor detection and segmentation using a wrapper based genetic algorithm for optimized feature set. Cluster Comput 2019; 22(6): 13369-80.
[16]
Mustaqeem A, Javed A, Fatima T. An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. Int J Image Graph Signal Process 2012; 4(10): 34-9.
[http://dx.doi.org/10.5815/ijigsp.2012.10.05]
[17]
Stupp R, Roila F. Malignant glioma: ESMO clinical recommendations for diagnosis, treatment and follow-up. Ann Oncol 2008; 19 (Suppl. 2): ii83-5.
[http://dx.doi.org/10.1093/annonc/mdn099] [PMID: 18456781]
[18]
Nasel C. Imaging of brain tumors with histological correlations, A. Drevelegas, Springer, 2003. 302 pp.;€ 129, ISBN-No. 3-540-41092-9. Eur J Radiol 2004; 49(3): 287.
[http://dx.doi.org/10.1016/S0720-048X(03)00140-2]
[19]
Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 2015; 34(10): 1993-2024.
[http://dx.doi.org/10.1109/TMI.2014.2377694] [PMID: 25494501]
[20]
Ramadas M, Abraham A. Detecting tumours by segmenting MRI images using transformed differential evolution algorithm with Kapur’s thresholding. Neural Comput Appl 2020; 32: 6139-49.
[21]
Rathi VP, Palani S. Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis. arXiv 2012.
[22]
Wadhwa A, Bhardwaj A, Singh Verma V. A review on brain tumor segmentation of MRI images. Magn Reson Imaging 2019; 61: 247-59.
[http://dx.doi.org/10.1016/j.mri.2019.05.043] [PMID: 31200024]
[23]
Hussain UN, Khan MA, Lali IU, et al. A unified design of ACO and skewness-based brain tumor segmentation and classification from MRI scans. J Control Eng Appl Inform 2020; 22(2): 43-55.
[24]
Zhang W, Li R, Deng H, et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 2015; 108: 214-24.
[http://dx.doi.org/10.1016/j.neuroimage.2014.12.061] [PMID: 25562829]
[25]
Deepika K, Bodapati JD, Srihitha RK. An efficient automatic brain tumor classification using LBP features and SVM-based classifier. In: Chaki N, Devarakonda N, Sarkar A, Debnath N, Eds. Proceedings of International Conference on Computational Intelligence and Data Engineering. Singapore: Springer 2019; pp. 163-70.
[http://dx.doi.org/10.1007/978-981-13-6459-4_17]
[26]
García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal 2013; 17(1): 1-18.
[http://dx.doi.org/10.1016/j.media.2012.09.004] [PMID: 23084503]
[27]
El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM. Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Syst Appl 2014; 41(11): 5526-45.
[http://dx.doi.org/10.1016/j.eswa.2014.01.021]
[28]
de Brebisson A, Montana G. Deep neural networks for anatomical brain segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2015 Jun 7-12; Boston MA, USA.. 20-8.
[http://dx.doi.org/10.1109/CVPRW.2015.7301312]
[29]
Smistad E, Falch TL, Bozorgi M, Elster AC, Lindseth F. Medical image segmentation on GPUs--a comprehensive review. Med Image Anal 2015; 20(1): 1-18.
[http://dx.doi.org/10.1016/j.media.2014.10.012] [PMID: 25534282]
[30]
Kazemi K, Noorizadeh N. Quantitative comparison of SPM, FSL, and brainsuite for brain MR image segmentation. J Biomed Phys Eng 2014; 4(1): 13-26.
[PMID: 25505764]
[31]
Foster B, Bagci U, Mansoor A, Xu Z, Mollura DJ. A review on segmentation of positron emission tomography images. Comput Biol Med 2014; 50: 76-96.
[http://dx.doi.org/10.1016/j.compbiomed.2014.04.014] [PMID: 24845019]
[32]
Grover VP, Tognarelli JM, Crossey MM, Cox IJ, Taylor-Robinson SD, McPhail MJ. Magnetic resonance imaging: Principles and techniques: Lessons for clinicians. J Clin Exp Hepatol 2015; 5(3): 246-55.
[http://dx.doi.org/10.1016/j.jceh.2015.08.001] [PMID: 26628842]
[33]
Vion-Dury J, Meyerhoff DJ, Cozzone PJ, Weiner MW. What might be the impact on neurology of the analysis of brain metabolism by in vivo magnetic resonance spectroscopy? J Neurol 1994; 241(6): 354-71.
[http://dx.doi.org/10.1007/BF02033352] [PMID: 7931430]
[34]
Mandal D, Chatterjee A, Maitra M. Robust medical image segmentation using particle swarm optimization aided level set based global fitting energy active contour approach. Eng Appl Artif Intell 2014; 35: 199-214.
[http://dx.doi.org/10.1016/j.engappai.2014.07.001]
[35]
Gao H, Xu W, Sun J, Tang Y. Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm. IEEE Trans Instrum Meas 2009; 59(4): 934-46.
[http://dx.doi.org/10.1109/TIM.2009.2030931]
[36]
Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vis 1988; 1(4): 321-31.
[http://dx.doi.org/10.1007/BF00133570]
[37]
Jonasson L, Hagmann P, Pollo C, et al. A level set method for segmentation of the thalamus and its nuclei in DT-MRI. Signal Processing 2007; 87(2): 309-21.
[http://dx.doi.org/10.1016/j.sigpro.2005.12.017]
[38]
Raju AR, Suresh P, Rao RR. Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern Biomed Eng 2018; 38(3): 646-60.
[http://dx.doi.org/10.1016/j.bbe.2018.05.001]
[39]
Mesejo P, Valsecchi A, Marrakchi-Kacem L, Cagnoni S, Damas S. Biomedical image segmentation using geometric deformable models and metaheuristics. Comput Med Imaging Graph 2015; 43: 167-78.
[http://dx.doi.org/10.1016/j.compmedimag.2013.12.005] [PMID: 24480648]
[40]
Jifeng N, Chengke W, Shigang L, Shuqin Y. NGVF: An improved external force field for active contour model. Pattern Recognit Lett 2007; 28(1): 58-63.
[http://dx.doi.org/10.1016/j.patrec.2006.06.014]
[41]
Maksoud EAA, Elmogy MM, Al-Awadi RM. Segmentation of brain tumor from MRI images based on hybrid clustering techniques. In: Hassanien AE, Gaber T, Eds. Handbook of Research on Machine Learning Innovations and Trends. Pennsylvania, United States: IGI Global 2017; pp. 114-35.
[http://dx.doi.org/10.4018/978-1-5225-2229-4.ch006]
[42]
Zhao Y, Yan Q, Yang Z, Yu X, Jia B. A novel artificial bee colony algorithm for structural damage detection. Adv Civ Eng 2020; 2020: 2020.
[http://dx.doi.org/10.1155/2020/3743089]
[43]
Talo M, Baloglu UB. Yıldırım Ö, Acharya UR. Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res 2019; 54: 176-88.
[http://dx.doi.org/10.1016/j.cogsys.2018.12.007]
[44]
Hasan SK, Ahmad M. Two-step verification of brain tumor segmentation using watershed-matching algorithm. Brain Inform 2018; 5(2): 1-11.
[http://dx.doi.org/10.1186/s40708-018-0086-x] [PMID: 29881932]
[45]
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]
[46]
Mekhmoukh A, Mokrani K. Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation. Comput Methods Programs Biomed 2015; 122(2): 266-81.
[http://dx.doi.org/10.1016/j.cmpb.2015.08.001] [PMID: 26299609]
[47]
Sumathi R, Venkatesulu M, Arjunan SP. Extracting tumor in MR brain and breast image with Kapur’s entropy based Cuckoo Search Optimization and morphological reconstruction filters. Biocybern Biomed Eng 2018; 38(4): 918-30.
[http://dx.doi.org/10.1016/j.bbe.2018.07.005]
[48]
Li J, Yu ZL, Gu Z, Liu H, Li Y. MMAN: Multi-modality aggregation network for brain segmentation from MR images. Neurocomputing 2019; 358: 10-9.
[http://dx.doi.org/10.1016/j.neucom.2019.05.025]
[49]
Razzak MI, Imran M, Xu G. Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inform 2019; 23(5): 1911-9.
[http://dx.doi.org/10.1109/JBHI.2018.2874033] [PMID: 30295634]
[50]
Zulpe NS, Pawar VP. Review on brain tumor segmentation and classification techniques. Int J Eng Res Technol (Ahmedabad) 6: 1-5.
[51]
Kumar A, Ashok A, Ansari MA. Brain tumor classification using hybrid model of PSO and SVM classifier. 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). 1022-6.
[http://dx.doi.org/10.1109/ICACCCN.2018.8748787]
[52]
Havaei M, Dutil F, Pal C, Larochelle H, Jodoin PM. A convolutional neural network approach to brain tumor segmentation BrainLes. Cham: Springer 2015; pp. 195-208.
[53]
Shelhamer E, Long J, Darrell T. Fully Convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39(4): 640-51.
[http://dx.doi.org/10.1109/TPAMI.2016.2572683] [PMID: 27244717]
[54]
Jothi G. Hybrid Tolerance Rough Set–Firefly based supervised feature selection for MRI brain tumor image classification. Appl Soft Comput 2016; 46: 639-51.
[http://dx.doi.org/10.1016/j.asoc.2016.03.014]
[55]
Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017; 36: 61-78.
[http://dx.doi.org/10.1016/j.media.2016.10.004] [PMID: 27865153]
[56]
Maksoud EAA, Elmogy M. 3D brain tumor segmentation based on hybrid clustering techniques using multi-views of MRI. In: Dey N, Bhateja V, Hassanien A, Eds. Medical Imaging in Clinical Applications. Cham: Springer 2016; pp. 81-104.
[http://dx.doi.org/10.1007/978-3-319-33793-7_4]
[57]
Narayanan A, Rajasekaran MP, Zhang Y, Govindaraj V, Thiyagarajan A. Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybern Biomed Eng 2019; 39(2): 350-81.
[http://dx.doi.org/10.1016/j.bbe.2018.12.003]
[58]
Jalab HA, Hasan AM. Magnetic resonance imaging segmentation techniques of brain tumors: A review. Arch Neurosci 2019; 6(49): 1-10.
[59]
Rao GS, Vydeki D. Brain tumor detection approaches: A review. In: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT). 2018 Dec 13-14; Tirunelveli, India.. 479-88.
[60]
Lather M, Singh P. Investigating brain tumor segmentation and detection techniques. Procedia Comput Sci 2020; 167: 121-30.
[http://dx.doi.org/10.1016/j.procs.2020.03.189]
[61]
Tarkhaneh O, Shen H. An adaptive differential evolution algorithm to optimal multi-level thresholding for MRI brain image segmentation. Expert Syst Appl 2019; 138: 112820.
[http://dx.doi.org/10.1016/j.eswa.2019.07.037]
[62]
Aswathy SU, Devadhas GG, Kumar SS. A tumor segmentation approach from FLAIR MRI brain images using SVM and genetic algorithm. Int J Biomed Eng Technol 2020; 33(4): 386-97.
[http://dx.doi.org/10.1504/IJBET.2020.108993]
[63]
Fang F, Yao Y, Zhou T, Xie G, Lu J. Self-supervised multi-modal hybrid fusion network for brain tumor segmentation. IEEE J Biomed Health Inform 2021; 1.
[http://dx.doi.org/10.1109/JBHI.2021.3109301] [PMID: 34478389 ]
[64]
Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 2013; 31(8): 1426-38.
[http://dx.doi.org/10.1016/j.mri.2013.05.002] [PMID: 23790354]
[65]
Tahir B, Iqbal S, Usman Ghani Khan M, et al. Feature enhancement framework for brain tumor segmentation and classification. Microsc Res Tech 2019; 82(6): 803-11.
[http://dx.doi.org/10.1002/jemt.23224] [PMID: 30768835]
[66]
Nabizadeh N, Kubat M. Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comput Electr Eng 2015; 45: 286-301.
[http://dx.doi.org/10.1016/j.compeleceng.2015.02.007]
[67]
Sehgal A, Goel S, Mangipudi P, Mehra A, Tyagi D. Automatic brain tumor segmentation and extraction in MR images. In: 2016 Conference on Advances in Signal Processing (CASP) 2016 Jun 9-11; Pune, India. 104-7.
[http://dx.doi.org/10.1109/CASP.2016.7746146]
[68]
Nayak DR, Dash R, Majhi B, Prasad V. Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach. Expert Syst Appl 2017; 88: 152-64.
[http://dx.doi.org/10.1016/j.eswa.2017.06.038]
[69]
Ilhan U, Ilhan A. Brain tumor segmentation based on a new threshold approach. Procedia Comput Sci 2017; 120: 580-7.
[http://dx.doi.org/10.1016/j.procs.2017.11.282]
[70]
Panda R, Agrawal S, Samantaray L, Abraham A. An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques. Appl Soft Comput 2017; 50: 94-108.
[http://dx.doi.org/10.1016/j.asoc.2016.11.011]
[71]
Sharma A, Kumar S, Singh SN. Brain tumor segmentation using DE embedded OTSU method and neural network. Multidimens Syst Signal Process 2019; 30(3): 1263-91.
[http://dx.doi.org/10.1007/s11045-018-0603-3]
[72]
Lu S, Sun C, Lu Z. An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling. Energy Convers Manage 2010; 51(3): 561-71.
[http://dx.doi.org/10.1016/j.enconman.2009.10.024]
[73]
Deng W, Xiao W, Deng H, Liu J. MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. In: 2010 3rd International Conference on Biomedical Engineering and Informatics 2010 Oct 16-18; Yantai, China. 393-6.
[http://dx.doi.org/10.1109/BMEI.2010.5639536]
[74]
Wang L, Wu H, Pan C. Region-based image segmentation with local signed difference energy. Pattern Recognit Lett 2013; 34(6): 637-45.
[http://dx.doi.org/10.1016/j.patrec.2012.12.022]
[75]
Bahadure NB, Ray AK, Thethi HP. Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging 2017; 2017: 9749108.
[http://dx.doi.org/10.1155/2017/9749108] [PMID: 28367213]
[76]
Udayakumar E, Santhi S, Gowrishankar R, Ramesh C, Gowthaman T. Region growing image segmentation for newborn brain MRI. Biotechnology. An Indian Journal Trade Sci Inc 2016; 12: 1-8.
[77]
Latif G, Iskandar DA, Alghazo J. Multiclass brain tumor classification using region growing based tumor segmentation and ensemble wavelet features. In: Proceedings of the 2018 International Conference on Computing and Big Data 2018 Sep 8-10; Charleston, USA. 67-72.
[http://dx.doi.org/10.1145/3277104.3278311]
[78]
Pei L, Bakas S, Vossough A, Reza SMS, Davatzikos C, Iftekharuddin KM. Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion. Biomed Signal Process Control 2020; 55: 101648.
[http://dx.doi.org/10.1016/j.bspc.2019.101648] [PMID: 34354762]
[79]
Maiti I, Chakraborty M. A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model. In: 2012 National Conference on Computing and Communication Systems 2012 Nov 21-22; Durgapur, India. 1-5.
[http://dx.doi.org/10.1109/NCCCS.2012.6413020]
[80]
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]
[81]
Weiskopf N, Sitaram R, Josephs O, et al. Real-time functional magnetic resonance imaging: Methods and applications. Magn Reson Imag 2007; 25(6): 989-1003.
[http://dx.doi.org/10.1016/j.mri.2007.02.007] [PMID: 17451904]
[82]
Hamad YA, Simonov K, Naeem MB. Brain’s tumor edge detection on low contrast medical images. In: 2018 1st Annual International Conference on Information and Sciences (AiCIS) 2018 Nov 20-21; Fallujah, Iraq. 45-50.
[http://dx.doi.org/10.1109/AiCIS.2018.00021]
[83]
Kaleem M, Sanaullah M, Hussain MA, Jaffar MA, Choi TS. Segmentation of brain tumor tissue using marker controlled watershed transform method. In: Chowdhry BS, Shaikh FK, Hussain DMA, Uqaili MA, Eds. International Multi Topic Conference. Berlin, Heidelberg: Springer 2012; pp. 222-7.
[http://dx.doi.org/10.1007/978-3-642-28962-0_22]
[84]
Anand A. Brain tumor segmentation using watershed technique and self-organizing maps. Indian J Sci Technol 2017; 10(44): 1-6.
[85]
Hasan AM, Meziane F, Aspin R, Jalab HA. Segmentation of brain tumors in MRI images using three-dimensional active contour without edge. Symmetry (Basel) 2016; 8(11): 132.
[http://dx.doi.org/10.3390/sym8110132]
[86]
Sivakumar V, Janakiraman N. A novel method for segmenting brain tumor using modified watershed algorithm in MRI image with FPGA. Biosystems 2020; 198: 104226.
[http://dx.doi.org/10.1016/j.biosystems.2020.104226] [PMID: 32861800]
[87]
Abdel-Maksoud E, Elmogy M, Al-Awadi R. Brain tumor segmentation based on a hybrid clustering technique. Egypt Inform J 2015; 16(1): 71-81.
[http://dx.doi.org/10.1016/j.eij.2015.01.003]
[88]
Agrawal R, Sharma M, Singh BK. Segmentation of brain tumour based on clustering technique: Performance analysis. J Intell Syst 2019; 28(2): 291-306.
[http://dx.doi.org/10.1515/jisys-2017-0027]
[89]
Zheng X, Lei Q, Yao R, Gong Y, Yin Q. Image segmentation based on adaptive K-means algorithm. EURASIP J Image Video Process 2018; 2018(1): 1-10.
[http://dx.doi.org/10.1186/s13640-018-0309-3]
[90]
Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognit Lett 2020; 129: 181-9.
[http://dx.doi.org/10.1016/j.patrec.2019.11.019]
[91]
Agravat RR, Raval MS. Deep learning for automated brain tumor segmentation in MRI images. In: Dey N, Ashour AS, Shi F, Balas VE, Eds. Soft Computing Based Medical Image Analysis. Cambridge, Massachusetts: Academic Press 2018; pp. 183-201.
[http://dx.doi.org/10.1016/B978-0-12-813087-2.00010-5]
[92]
Bal Abhishek, Banerjee Minakshi, Chakrabarti Amlan. Sharma S. MRI brain tumor segmentation and analysis using rough-fuzzy c-means and shape based properties. J King Saud Univ – Comput Inform Sci 2020; 32(10): 1218.
[93]
Gondra I, Cabria I. Automated segmentation of brain tumors in MRI using potential field clustering. In: International Conference on Computer as a Tool (EUROCON) 2015 Sep 8-11; Salamanca, Spain.. 1-6.
[94]
Angulakshmi M, Priya GL. Walsh Hadamard transform for Simple Linear Iterative Clustering (SLIC) superpixel based spectral clustering of multimodal MRI brain tumor segmentation. IRBM 2019; 40(5): 253-62.
[http://dx.doi.org/10.1016/j.irbm.2019.04.005]
[95]
Elyasi A, Hosseini M, Esfanyari M. Active contours in Brain tumor segmentation. J Appl Sci Res 2011; 7(7)
[96]
Abdelaziz E, Elhoussaine O, Abdenbi B. Brain tumor segmentation with Vander Lugt correlator based active contour. Comput Methods Prog Biomed 2018; 160: 103-17.
[http://dx.doi.org/10.1016/j.cmpb.2018.04.004]
[97]
Sheela CJJ, Suganthi G. Brain tumor segmentation with radius contraction and expansion based initial contour detection for active contour model. Multimed Tools Appl 2020; 79: 23793-819.
[98]
Natarajan P, Krishnan N, Kenkre NS, Nancy S, Singh BP. Tumor detection using threshold operation in MRI brain images. In: 2012 IEEE International Conference on Computational Intelligence and Computing Research. 2012 Dec 18-20; Coimbatore, India. 1-4.
[http://dx.doi.org/10.1109/ICCIC.2012.6510299]
[99]
Sehgal AA, Li Y, Lal B, et al. CEST MRI of 3-O-methyl-D-glucose uptake and accumulation in brain tumors. Magn Reson Med 2019; 81(3): 1993-2000.
[http://dx.doi.org/10.1002/mrm.27489] [PMID: 30206994]
[100]
Sukumaran A, Abraham A. Automated detection and classification of meningioma tumor from MR images using sea lion optimization and deep learning models. Axioms 2022; 11(1): 15.
[http://dx.doi.org/10.3390/axioms11010015]
[101]
Naik J, Patel S. Tumor detection and classification using decision tree in brain MRI. IJCSNS 2014; 14(6): 87-91.
[102]
Bhawar KA, Chhajed AS. Brain tumor classification using data mining algorithms. IJESR 2016; 5(11): 239-43.
[103]
Telrandhe SR, Pimpalkar A, Kendhe A. Detection of brain tumor from MRI images by using segmentation & SVM. In: 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave) 2016 Feb 29-Mar 1; Coimbatore, India. 1-6.
[http://dx.doi.org/10.1109/STARTUP.2016.7583949]
[104]
Garg G, Garg R. Brain tumor detection and classification based on hybrid ensemble classifier. arXiv preprint arXiv:210100216 2021.
[105]
Sukumaran A, Devadhas GG, Kumar SS. An improved tumor segmentation algorithm from T2 and FLAIR multimodality MRI brain images by support vector machine and genetic algorithm. Cogent Eng 2019; 5(1): 1470915.
[http://dx.doi.org/10.1080/23311916.2018.1470915]
[106]
Zaw HT, Maneerat N, Win KY. Brain tumor detection based on Naïve bayes classification. In: 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST) 2019 Jul 2-5; Luang Prabng, Laos. 1-4.
[http://dx.doi.org/10.1109/ICEAST.2019.8802562]
[107]
Reddy DS, Harshitha CN, Belinda CM. Brain tumor prediction using naïve Bayes’ classifier and decision tree algorithms. IJET 2018; 7(1): 137-41.
[108]
Kaur G, Oberoi A. Novel approach for brain tumor detection based on naïve bayes classification. In: Sharma N, Chakrabarti A, Balas V, Eds. Data Management, Analytics and Innovation. Singapore: Springer 2019.
[109]
naceur MB, Saouli R, Akil M, Kachouri R. Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images. Comput Methods Programs Biomed 2018; 166: 39-49.
[http://dx.doi.org/10.1016/j.cmpb.2018.09.007]
[110]
Rajam RA, Reshmi R, Suresh A, Suresh A, Sindhuja S. Segmentation and analysis of brain tumor using meta-heuristic algorithm. In: 2018 International Conference on Recent Trends in Electrical, Control and Communication (RTECC) 2018 Mar 20-22; Malaysia, Malaysia. 256-60.
[http://dx.doi.org/10.1109/RTECC.2018.8625653]
[111]
Natteshan NVS, Jothi AA. Automatic classification of brain MRI images using SVM and neural network classifiers. In: Ganguli S, Mahapatra PK, Kumar A, Eds. Artificial Immune System Based Image Enhancement Technique. New York: Springer International Publishing 2015.
[112]
Ghotekar B, Mahajan KJ. Brain tumor detection and classification using SVM. IRJET 2016; 3(03): 2395-0072.
[113]
Vani N, Sowmya A, Jayamma N. Brain tumor classification using support vector machine. IRJET 2017; 4(7): 1724-9.
[114]
Kumar S, Dabas C, Godara S. Classification of brain MRI tumor images: A hybrid approach. Procedia Comput Sci 2017; 122: 510-7.
[http://dx.doi.org/10.1016/j.procs.2017.11.400]
[115]
Pham TX, Siarry P, Oulhadj H. Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Appl Soft Comput 2018; 65: 230-42.
[http://dx.doi.org/10.1016/j.asoc.2018.01.003]
[116]
Raschke F, Fuster-Garcia E, Opstad KS, Howe FA. Classification of single-voxel 1H spectra of brain tumours using LCModel. NMR Biomed 2012; 25(2): 322-31.
[http://dx.doi.org/10.1002/nbm.1753] [PMID: 21796709]
[117]
Li Q, Yu Z, Wang Y, Zheng H. TumorGAN: A multi-modal data augmentation framework for brain tumor segmentation. Sensors 2020; 20(15): 4203.
[http://dx.doi.org/10.3390/s20154203]
[118]
Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 2020; 59: 221-30.
[http://dx.doi.org/10.1016/j.cogsys.2019.09.007]
[119]
Tong J, Zhao Y, Zhang P, Chen L, Jiang L. MRI brain tumor segmentation based on texture feature and kernel sparse coding. Biomed Signal Process Control 2019; 47: 387-92.
[120]
Rani N, Vashisth S. Brain tumor detection and classification with feed forward back-prop neural network. Int J Comput Appl 2016; 146(12): 1-6.
[121]
Seetha J, Selvakumar Raja S. Brain tumor classification using convolutional neural network. Biomed Pharmacol J 2018; 11(3): 1457-61.
[122]
Wu W, Li D, Du J, et al. An intelligent diagnosis method of brain MRI tumor segmentation using deep convolutional neural network and SVM algorithm. Comput Math Methods Med 2020; 2020: 6789306.
[http://dx.doi.org/10.1155/2020/6789306]
[123]
Badza MM, Barjaktarovic MC. Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci 2020; 10(6): 1999.
[124]
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]
[125]
Sharif MI, Li JP, Khan MA, Saleem MA. Active deep neural network feature selection for segmentation and recognition of brain tumor using MRI images. Pattern Recognit Lett 2020; 129: 181-9.
[126]
Machhale K, Nandpuru HB, Kapur V, Kosta L. MRI Brain Cancer Classification Using Hybrid Classifier (SVM-KNN). In: 2015 International Conference on Industrial Instrumentation and Control (ICIC). 2015 May 28-30; Pune, India. 2015.
[127]
Viswa Priya V. Indian J Sci Technol 2016; 9(19): 1-6.
[http://dx.doi.org/10.17485/ijst/2016/v9i47/106448]
[128]
Amin J, Sharif M, Yasmin M, Fernandes SL. A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognit Lett 2020; 139: 118-27.
[129]
Sonavane R, Sonar P. Classification and segmentation of brain tumor using adaboost classifier In: 2016 International Conference on Global Trends in Signal Processing Information Computing and Communication 2016 Dec 22-24; Jalgaon, India. 396-403.
[http://dx.doi.org/10.1109/ICGTSPICC.2016.7955334]
[130]
Vaishnavee KB, Amshakala K. An automated MRI brain image segmentation and tumor detection using SOM-clustering and Proximal Support Vector Machine classifier. In: 2015 IEEE International Conference on Engineering and Technology (ICETECH) 2015 Mar 20; Coimbatore, India. 1-6.
[http://dx.doi.org/10.1109/ICETECH.2015.7275030]
[131]
Shankar K, Ilayaraja M, Deepalakshmi P, Ramkumar S, Sathesh Kumar K. Effective MKSVM classifier with LDA methods for brain tumor detection in MR images. Int J Recent Technol Eng 2019; 8(4): 987-92.
[132]
Ito R, Nakae K, Hata J, Okano H, Ishii S. Semi-supervised deep learning of brain tissue segmentation. Neural Networks 2019; 116: 25-5.

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