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Health and Technology
Published in: Health and Technology 6/2020

06-08-2020 | Review Paper

A survey on machine learning based brain retrieval algorithms in medical image analysis

Authors: Arpit Kumar Sharma, Amita Nandal, Arvind Dhaka, Rahul Dixit

Published in: Health and Technology | Issue 6/2020

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Abstract

In recent times, researchers showed huge interest in machine learning approaches that attempts to develop the information representations via computational modules. Past decade gained momentum by deep learning approaches and their potential of enhancing the performance for numerous automation operations with superior future research applications. The novelties in medical image processing initialized the unique perspective to diagnose the human body with superior resolution and enhanced accuracy. This paper offers a comprehensive work on existing methodologies that attain optimum results in their respective domains. There exist various Magnetic Resonance Imaging (MRI) brain scan classifiers to obtain efficient features extraction images. The fundamental step in these methods includes several actions to be performed by using different approaches in order to characterize the anomalous developments in MRI scans of brain. Mostly, current techniques are utilizing deep learning feature extraction algorithm from MRI brain scans to obtain their relevant features. Currently, deep learning algorithms associated with medical imaging results in achieving remarkable performance enhancement in diagnosis as well as characterization of complex pathologies in case of brain tumors. This paper provides existing research gaps in identification, segmentation and feature extraction among current approaches. This paper also suggests the future directions to increase the efficiency of current models.
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Literature
1.
2.
Stewart BW, Wild CP. World cancer report 2014. Lyon, France: IARC; 2014.
3.
C. P. Wild, E. Weiderpass, B. W. Stewart, World cancer report: cancer research for Cancer prevention, world cancer reports, ISBN: 978-92-832-0447-3, 2020.
4.
Behin A, Hoang-Xuan K, Carpentier AF, Delattre J-Y. Primary brain tumours in adults. Lancet. 2003;361(9354):323–31.CrossRef
5.
Drevelegas A. Imaging of brain tumors with histological correlations. Berlin, Germany: Springer; 2002.CrossRef
6.
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. Dec. 2017;42:60–88.CrossRef
7.
Goodenberger ML, Jenkins RB. Genetics of adult glioma. Cancer Genet. Dec. 2012;205(12):613–21.CrossRef
8.
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. Jun. 2016;131(6):803–20.CrossRef
9.
Bishop C. Pattern recognition and machine learning. Berlin, Germany: Springer-Verlag; 2006.MATH
10.
H. H. Sultan, N. M. Salem and W. AL-ATABANYSSM, “Multi-Classification of Brain Tumor Images Using Deep Neural Network”, Special Section On Deep Learning For Computer-Aided Medical Diagnosis, vol. 7, 2019.
11.
T. Rajesh and R. S. M. Malar, “Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images”, in Proc. Int. Conf. Adv. Nanomaterials Emerg. Eng. Technol. (ICANMEET), Jul. 2013, pp. 240–244.
12.
K. Machhale, H. B. Nandpuru, V. Kapur, and L. Kosta, “MRI brain cancer classication using hybrid classier (SVM-KNN)”, in Proc. Int. Conf. Ind. Instrum. Control (ICIC), May 2015, pp. 60–65.
13.
M. Shasidhar, V. S. Raja, and B. V. Kumar. MRI brain image segmentation using modied fuzzy C-means clustering algorithm. in Proc Int Conf Commun Syst Netw Technol (CSNT), Jun. 2011, pp. 473–478.
14.
S. Goswami and L. K. P. Bhaiya. Brain tumour detection using unsupervised learning based neural network. in Proc Int Conf Commun Syst Netw Technol (CSNT), Apr. 2013, pp. 573–577.
15.
S. Khalid, T. Khalil, and S. Nasreen. A survey of feature selection and feature extraction techniques in machine learning, in Proc Sci Inf Conf, Aug. 2014, pp. 372–378.
16.
17.
18.
Matsugu M, Mori K, Mitari Y, Kaneda Y. Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. Jul. 2003;16(56):555–9.CrossRef
19.
20.
LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. Nov. 1998;86(11):2278–324.CrossRef
21.
A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks, in Proc Adv Neural Inf Process Syst (NIPS), Jan. 2012, pp. 1097–1105.
22.
Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. Dec. 2009;62(6):1609–18.CrossRef
23.
El-Dahshan ESA, Hosny T, Salem ABM. Hybrid intelligent techniques for MRI brain images classification. Digit Signal Process. Mar. 2010;20:433–41.CrossRef
24.
Cheng J, Huang W, Cao S, Yang R, Yang W, Yun Z, et al. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS ONE. Oct. 2015;10(10):Art no e0140381.CrossRef
25.
Ertosun MG, Rubin DL. “Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks”, in Proc. AMIA Annu Symp Proc. Nov. 2015;2015:1899–908.
26.
27.
28.
Anaraki AK, Ayati M, Kazemi F. Magnetic resonance imaging based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics Biomed Eng. Mar. 2019;39(1):63–74.CrossRef
29.
Hasan AM, Meziane F. Automated screening of MRI brain scanning using grey level statistics. Comput. Elect. Eng. Jul. 2016;53:276–91.CrossRef
30.
Nabizadeh N, Kubat M. Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. J Comput Elect Eng. Jul. 2015;45:286–301.CrossRef
31.
Sachdeva J, Kumar V, Gupta I, Khandelwal N, Ahuja CK. A package-SFERCB-`segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors. Appl Soft Comput. Oct. 2016;47:151–67.CrossRef
32.
Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MR. Z Med Phys. 2019;29(2):102–27.CrossRef
33.
34.
Chen Y, Jiang H, Li C, Jia X, Ghamisi P. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens. Oct. 2016;54(10):6232–51.CrossRef
35.
36.
B. Wicht. Deep learning feature extraction for image processing, Ph.D. dissertation, Dept. Inform., Univ. Fribourg, Fribourg, Switzerland, 2017.
37.
Ari A, Hanbay D. Deep learning based brain tumor classification and detection system. Turkish J Elect Eng Comput Sci. 2018;26(5):2275–86.CrossRef
38.
Huang M, Yang W, Yu M, Lu Z, Feng Q, Chen W. Retrieval of brain tumors with region-specific bag-of-visual-words representations in contrast-enhanced MRI images. Computational and mathematical methods in medicine, vol. 2012;2012.
39.
Yang W, Feng Q, Yu M, Lu Z, Gao Y, Xu Y, et al. Content-based retrieval of brain tumor in contrast-enhanced MRI images using tumor margin information and learned distance metric. Med Phys. 2012;39:6929–42.CrossRef
40.
Huang M, Yang W, Wu Y, Jiang J, Gao Y, Chen Y, et al. Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images. PLoS One. 2014;9:e102754.CrossRef
41.
Cheng J, Yang W, Huang M, Huang W, Jiang J, Zhou Y, et al. Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS One. 2016;11:e0157112.CrossRef
42.
Greenspan H, Pinhas AT. Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Trans Inf Technol Biomed. 2007;11:190–202.CrossRef
43.
Rahman MM, Desai BC, Bhattacharya P. Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Comput Med Imaging Graph. 2008;32:95–108.CrossRef
44.
Iakovidis DK, Pelekis N, Kotsifakos EE, Kopanakis I, Karanikas H, Theodoridis Y. A pattern similarity scheme for medical image retrieval. IEEE Trans Inf Technol Biomed. 2009;13:442–50.CrossRef
45.
Jiang J, Wu Y, Huang M, Yang W, Chen W, Feng Q. 3D brain tumor segmentation in multimodal MR images based on learning population-and patient-specific feature sets. Comput Med Imaging Graph. 2013;37:512–21.CrossRef
46.
Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," nature, vol. 521, p. 436, 2015.
47.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097–1105.
48.
K. Simonyan and A. Zisserman, "very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:14091556, 2014.
49.
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis. 2015;115:211–52.MathSciNetCrossRef
50.
Xiao T, Liu L, Li K, Qin W, Yu S, Li Z. Comparison of transferred deep neural networks in ultrasonic breast masses discrimination. BioMed Research International, vol. 2018;2018.
51.
A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN features off-the-shelf: an astounding baseline for recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2014, pp. 806–813.
52.
L. D. Nguyen, D. Lin, Z. Lin, and J. Cao, “Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation,” Circuits and Systems (ISCAS), 2018 IEEE International Symposium on, 2018, pp. 1–5.
53.
Khatami A, Babaie M, Tizhoosh HR, Khosravi A, Nguyen T, Nahavandi S. A sequential search-space shrinking using CNN transfer learning and a radon projection pool for medical image retrieval. Expert Syst Appl. 2018;100:224–33.CrossRef
54.
Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, et al. Interactive medical image segmentation using deep learning with image-specific fine-tuning: IEEE Transactions on Medical Imaging; 2018.
55.
Swati ZNK, Zhao Q, Kabir M, Ali F, Zakir A, Ahmad S, et al. “Content-based brain tumor retrieval for MR images using transfer learning”, IEEE journals & magazines. Vol. 2019;7.
56.
Nyúl LG, Udupa JK, Zhang X. New variants of a method of MRI scale standardization. IEEE Trans Med Imag. Feb. 2000;19(2):143–50.CrossRef
57.
L. Bottou, Large-scale machine learning with stochastic gradient descent, Proc. COMPSTAT. Physica-Verlag HD, 2010, pp. 177–186.
58.
Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge, MA, USA: MIT press; 2016.MATH
59.
R. Colen, et al., “Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project,” BMC Medical Genomics. vol. 7, no. 30, June 2014.
60.
Velazquez ER, et al. Fully automatic GBM segmentation in the TCGA-GBM dataset: prognosis and correlation with VASARI features. Nat Sci Rep. Nov. 2015;5:16822.CrossRef
61.
Kumar R, Gupta A, Arora HS, Pandian GN, Raman B. CGHF: a computational decision support system for Glioma classification using hybrid Radiomics- and stationary wavelet-based features. IEEE Access. April 2020;8:79440–58.CrossRef
62.
Chang H, Zhong C, Han J, Mao JH. Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications. IEEE Trans Pattern Anal Mach Intell. May 2018;40(5):1182–94.CrossRef
63.
Nie D, et al. Estimating CT image from MRI data using 3D. International Workshop on Deep Learning in Medical Image Analysis, DLMIA. Sep. 2016;2016:170–8.
64.
M. I. Razzak, M. Imran, G. Xu, “Efficient Brain Tumor Segmentation with Multiscale Two-Pathway-Group Conventional Neural Networks”, Journal of Latex Class Files, vol. 6, no. 1, January 2007.
65.
A. M. Hasan, H. A. Jalab, F. Meziane, H. Kahtan, and A. S. Al-Ahmad, “Combining Deep and Handcrafted Image Features for MRI Brain Scan Classification”, IEEE Access, vol. 7, 2019.
66.
Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. An open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. March 2015;42(2):1341–53.CrossRef
67.
N. Nabizadeh, “Automated brain lesion detection and segmentation using magnetic resonance images,” Ph.D. dissertation, Dept. Elect. Comput. Eng., Univ. Miami, Coral Gables, FL, USA, 2015.
Metadata
Title
A survey on machine learning based brain retrieval algorithms in medical image analysis
Authors
Arpit Kumar Sharma
Amita Nandal
Arvind Dhaka
Rahul Dixit
Publication date
06-08-2020
Publisher
Springer Berlin Heidelberg
Published in
Health and Technology / Issue 6/2020
Print ISSN: 2190-7188
Electronic ISSN: 2190-7196
DOI
https://doi.org/10.1007/s12553-020-00471-0

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