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Review | Open Access

Brain Tumor Detection from Medical Images: A Survey

Muhammad Aqeel Aslam, Daxiang Cui( )
Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract

Nowadays, medical image processing has been one of the most challenging emerging field. Magnetic resonance imaging (MRI) technique is now widely used to detect brain tumor. This survey provides different strategies to detect and extract the brain tumor signal. The strategy for detecting and extracting the brain tumor signals is based on the MRI scanned images of the cerebrum. These methods incorporate some noise removal functions, segmentations and morphological operations which are the fundamental concepts of image processing. Detection and extraction of tumor signal from MRI scanned images of the cerebrum are carried out by MATLAB.

Keywords

Medical imagesMedical resonance imaging (MRI)Machine learningComputer visionSegmentationMorphology

References

[1]

A.M. Gardeck, J. Sheehan, W.C. Low, Immune and viral therapies for malignant primary brain tumors. Expert Opinion on Biological Therapy, 2017, 17(4): 457-474.
Crossref Google Scholar
[2]

G. Spena, P. Schucht, K. Seidel, et al., , Brain tumors in eloquent areas: A European multicenter survey of intraoperative mapping techniques, intraoperative seizures occurrence, and antiepileptic drug prophylaxis. Neurosurgical Review, 2017, 40, 287-298.
Crossref Google Scholar
[3]

H. D. Bailey, P. Rios, B. Lacour, et al., , Factors related to pregnancy and birth and the risk of childhood brain tumours: The ESTELLE and ESCALE studies (SFCE, France). International Journal of Cancer, 2017, 140: 1757-1769
Crossref Google Scholar
[4]

R. Siegel, D. Naishadham, and A. Jemal, Cancer statistics, 2013. CA Cancer J Clin, 2013, 63: 11-30.
Crossref Google Scholar
[5]

R. Weissleder, M. J. Pittet, Imaging in the era of molecular oncology. Nature, 2008, 452: 580-589
Crossref Google Scholar
[6]

R. Weissleder, Molecular imaging in cancer. Science, 2006, 312(5777): 1168-1171.
Crossref Google Scholar
[7]

J. Conde, A. Ambrosone, V. Sanz, et al., Design of multifunctional gold nanoparticles for in vitro and in vivo gene silencing. ACS Nano, 2012, 6(9): 8316-8324.
Crossref Google Scholar
[8]

S. Gambhir, Whole-animal imaging: The whole picture. Nature, 2010, 463(7283): 977-980.
Crossref Google Scholar
[9]

A. Ale, V. Ermolayev, E. Herzog. et al., FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-X-ray computed tomography. Nature Methods, 2012, 9: 615-620.
Crossref Google Scholar
[10]

C.L. Zhang, C. Li, Y.L Liu, et al., Gold nanoclusters-based nanoprobes for simultaneous fluorescence imaging and targeted photodynamic therapy with superior penetration and retention behavior in tumors. Adv Fun Mater, 2015, 25(8): 1314-1325.
Crossref Google Scholar
[11]

B.Z. Shen, Systems molecular imaging: right around the corner. Nano Biomed Eng, 2014, 6(1): 1-6.
Crossref Google Scholar
[12]

X. Lin, G. Hu, H. Fu. Et al., Emerging role of tumor-associated macrophages as therapeutic targets in glioblastoma multiforme. Nano Biomed Eng, 2014, 6(1): 7-18.
Crossref Google Scholar
[13]

H. Yan, G. Chang, T. Sun, et al., Molecular communication in nanonetworks. Nano Biomed Eng, 2016, 8(4): 274-287
Crossref Google Scholar
[14]

F. Mahmood, H.H. Johannesen, P. Geertsen, et al., Repeated diffusion MRI reveals earliest time point for stratification of radiotherapy response in brain metastases. Phys Med Biol, 2017, 62(8): 2990-3002.
Crossref Google Scholar
[15]

M. Grech-Sollars, B. Vaqas, G. Thompson, et al., An MRS- and PET-guided biopsy tool for intraoperative neuronavigational systems. J Neurosurg, 2017, 17: 1-7.
Crossref Google Scholar
[16]

L. Keith, B.D. Ross, C.J. Galbán, et al., Semiautomated workflow for clinically streamlined glioma parametric response mapping. Tomography, 2016, 2(4): 267-275.
Crossref Google Scholar
[17]

J. Zhang, F. Xia, Y. Yang, et al., Human CIK cells loaded with gold nanoprisms as theranostic platform for targeted photoacoustic imaging and enhancedimmuno-photothermal combined therapy. Nano Biomed Eng, 2016, 8(3): 112-127.
Crossref Google Scholar
[18]

S. Chen, C. Bao, C. Zhang, et al., EGFR antibody conjugated bimetallic Au@Ag nanorods for enhanced SERS-based tumor boundary identification targeted photoacoustic imaging and photothermal therapy, Nano Biomed Eng, 2016, 8(4): 315-328.
Crossref Google Scholar
[19]

F. Imanparast, M. Doosti, M.A. Faramarzi, Metal nanoparticles in atherosclerosis: applications and potential toxicity, Nano Biomed Eng, 2015, 7(3): 111-127.
Crossref Google Scholar
[20]

M. Boucher, F. Geffroy, S. Prévéral, et al., Genetically tailored magnetosomes used as MRI probe for molecular imaging of brain tumor, Biomaterials, 2016, 121: 167-178.
Crossref Google Scholar
[21]

J. Ferda, E. Ferdova, O. Hes, et al., PET/MRI: Multiparametric imaging of brain tumors. European Journal of Rradiology, 2017: Epub ahead of print.
Crossref Google Scholar
[22]

C. Wang, F.A. Schroeder, J.M. Hooker, Development of new positron emission tomography radiotracer for BET imaging. ACS Chemical Neuroscience, 2017, 8 (1): 17-21.
Crossref Google Scholar
[23]

T. Wang, Y. Hu, L. Zhang, et al., Erythropoietin nanopariticles. therapy for cerebral ischemic injury and metabolize in kidney and liver. Nano Biomed Eng, 2010; 2(1): 31-39
Crossref Google Scholar
[24]

J.Y. Kim, R.A. Gatenby, Quantitative clinical imaging methods for monitoring intratumoral evolution. Methods Mol Biol, 2017, 1513: 61-81
Crossref Google Scholar
[25]

D. Qiu, Y. Xing, and L. Zhang, Asymptotically stability of solutions of fuzzy differential equations in the quotient space of fuzzy numbers. Journal of Computational Analysis & Applications, 2017, 23(1): 1242-1251.
Google Scholar
[26]

C. Bund, C. Heimburger, A. Imperiale, et al., FDOPA PET-CT of nonenhancing brain tumors. Clinical Nuclear Medicine, 2017, 42(4): 250-257.
Crossref Google Scholar
[27]

V.J. Nagalkar, S.S. Asole, Brain tumor detection using digital image processing based on soft computing. Journal of Signal and Image Processing. 2012, 3(3): 102-105.
Google Scholar
[28]

S. Cheng, D. Cui, Accurate non rigid registration of lung images based on mutual information. Nano BioMed Eng, 2015, 7(4): 153-159.
Crossref Google Scholar
[29]
R.C. Patil, A.S. Bhalchandra, Brain tumor extraction from MRI images using MATLAB. International Journal of Electronics, Communication and Soft Computing Science and Engineering, 2(1).
[30]
M.L. Toure, Advanced algorithm for brain segmentation using fuzzy to localize cancer and epilepsy region. Proceedings of International Conference on Electronics and Information Engineering (ICEIE). Kyoto, Japan, 1-3 Aug. 2010.
Crossref
[31]

S. Murugavalli, V. Rajamani, A high speed parallel fuzzy c mean algorithm for brain tumor segmentation. BIME Journal, 2006, 6(1): 29-34.
Google Scholar
[32]

D.A. Dahab, S.S.A. Ghoniemy, and G.M. Selim, Automated brain tumor detection and identification using image processing and probabilistic neural network techniques. International Journal of Image Processing and Visual Communication, 2012, 1(2): 1-8.
Google Scholar
[33]

A. Aslam, E. Khan, and M.M.S. Beg, Improved edge detection algorithm for brain tumor segmentation. Procedia Computer Science, 2015, 58: 430-437.
Crossref Google Scholar
[34]

A. Mustaqeem, A. Javed, and T. Fatima, An efficient brain tumour detection algorithm using watershed & thresholding based segmentation. International Journal Image, Graphics and Signal Processing. 2012, 10: 34-39.
Crossref Google Scholar
[35]
H.M. Moftah, A.E. Hassanien, and M. Shoman, 3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms. Proceedings of 10th International Conference on Intelligent Systems Design and Applications (ISDA). Cairo, Egypt, 29 Nov. 1 Dec. 2010: 320-324,
[36]

P. Vasuda, S.Satheesh, Improved fuzzy C-means algorithm for MR brain image segmentation. International Journal on Computer Science and Engineering (IJCSE), 2010, 2(5): 1713-1715.
Google Scholar
[37]
N.N. Gopal, M. Karnan, Diagnose brain tumor through MRI using image processing clustering algorithms such as fuzzy C means along with intelligent optimization techniques. Proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). Coimbatore, India, 28-29 Dec. 2010: 1-4.
Crossref
[38]
D.M. Joshi, N.K. Rana, and V.M. Misra, Classification of brain cancer using artificial neural network. Proceedings of International Conference on Electronic Computer Technology (ICECT). Kuala Lumpur, Malaysia, 7-10 May 2010: 112-116.
[39]
M.N. Wu, C.C. Lin, and C.C. Chang, Brain Tumor detection using color-based K-means clustering segmentation. Proceedings of 3rd International Conference on International Information Hiding and Multimedia Signal Processing (ⅡH-MSP). Kaohsiung, Taiwan, 26-28 Nov. 2007: 245-250.
[40]

T. Logeswari, M. Karnan, An improved implementation of brain tumor detection using segmentation based on soft computing. Journal of Cancer Research and Experimental Oncology, 2010, 2(1): 6-14.
Crossref Google Scholar
[41]

H. Fan-Minogue, Z. Cao, R. Paulmurugan, et al., Noninvasive molecular imaging of c-Myc activation in living mice. Proc Natl Acad Sci USA., 2010, 107(36): 15892-15897.
Crossref Google Scholar
Nano Biomedicine and Engineering
Volume 9 Issue 1,
March 2017
Pages 72-81
DOI: 10.5101/nbe.v9i1.p72-81
Cite this article:
Aqeel Aslam M, Cui D. Brain Tumor Detection from Medical Images: A Survey. Nano Biomedicine and Engineering, 2017, 9(1): 72-81. https://doi.org/10.5101/nbe.v9i1.p72-81

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Received: 25 March 2017
Accepted: 30 March 2017
Published: 30 March 2017
© 2017 Muhammad Aqeel Aslam, Daxiang Cui.

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.
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