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Reviews in the Neurosciences

Editor-in-Chief: Huston, Joseph P.

Editorial Board: Topic, Bianca / Adeli, Hojjat / Buzsaki, Gyorgy / Crawley, Jacqueline / Crow, Tim / Gold, Paul / Holsboer, Florian / Korth, Carsten / Li, Jay-Shake / Lubec, Gert / McEwen, Bruce / Pan, Weihong / Pletnikov, Mikhail / Robbins, Trevor / Schnitzler, Alfons / Stevens, Charles / Steward, Oswald / Trojanowski, John


IMPACT FACTOR 2017: 2.590
5-year IMPACT FACTOR: 3.078

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Volume 30, Issue 1

Issues

Segmentation and clustering in brain MRI imaging

Golrokh Mirzaei
  • Department of Computer Science and Engineering, The Ohio State University, Marion, OH 43302, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hojjat Adeli
  • Corresponding author
  • Departments of Biomedical Informatics, Neurology, Neuroscience, The Ohio State University, Columbus, OH 43210, USA
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-09-27 | DOI: https://doi.org/10.1515/revneuro-2018-0050

Abstract

Clustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.

Keywords: clustering; convolutional neural network; FCM; K-means; segmentation

References

  • Abbasi, H., Bennet, L., Gunn, A.J., and Unsworth, C.P. (2017). Robust wavelet stabilized footprints of uncertainty for fuzzy system classifiers to automatically detect sharp waves in the eeg after hypoxia ischemia. Int. J. Neural Syst. 27, 1650051 (16 pages).PubMedCrossrefGoogle Scholar

  • Abdel-Maksoud, E., Elmogy, M., and Al-Awadi, R. (2015). Brain tumor segmentation based on a hybrid clustering technique. Egypt. Inform. J. 16, 71–81.CrossrefGoogle Scholar

  • Adeli, H. and Hung, S.L. (1995). Machine Learning – Neural Networks, Genetic Algorithms, and Fuzzy Systems (New York, USA: John Wiley and Sons).Google Scholar

  • Adeli, H. and Ghosh-Dastidar, S. (2004). Mesoscopic-wavelet freeway work zone flow and congestion feature extraction model. J. Transport. Eng. 130, 94–103.CrossrefGoogle Scholar

  • Aggarwal, C.C. and Reddy, C.K. (2014). Data Clustering: Algorithms and Applications (Boca Raton, FL, USA: Chapman & Hall/CRC), 617 pages.Google Scholar

  • Agrawal, R., Gehrke, J., Gunopulos, D., and Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications. ACM SIGMOD International Conference on Management of Data, Washington, USA, pp. 94–105.Google Scholar

  • Ahmadlou, M. and Adeli, H. (2010). Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr. Comput. Aided Eng. 17, 197–210.CrossrefGoogle Scholar

  • Ahmmed, R. and Hossain, F. (2016). Tumor detection in brain MRI image using template based K-means and Fuzzy C-means clustering algorithm. IEEE International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1–6.Google Scholar

  • Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., and Erickson, B.J. (2017). Deep learning for brain mri segmentation: state of the art and future directions. J. Digit. Imag. 30, 449–459.CrossrefGoogle Scholar

  • Alexandridis, A., Paizis, E., Chondrodima, E., and Stogiannos, M. (2017). A particle swarm optimization approach in printed circuit board thermal design. Integr. Comput. Aided Eng. 24, 143–155.CrossrefGoogle Scholar

  • Alhasan, A., White, D.J., and De Brabanter, K. (2016). Wavelet filter design for pavement roughness analysis. Comput. Aided Civ. Inf. Eng. 31, 907–920.CrossrefGoogle Scholar

  • Ali, S.I. and Shahzad, W. (2012). A feature subset selection on method based on conditional mutual information and ant colony optimization. Int. J. Comput. Appl. 60, 5–10.Google Scholar

  • Alshawi, T.A.., Elsamie, F.E., and Alshebeili, S.A. (2015). Magnetic resonance and computed tomography image fusion using bidimensional empirical mode decomposition. 2015 IEEE Global Conference on Signal and Information processing, Orlando, FL, USA, pp. 413–417.Google Scholar

  • Aydin, S., Demitras, S., Ates, K., and Tunga, M.A. (2016). Emotion recognition with eigen features of frequency band activities embedded in induced brain oscillations mediated by affective pictures. Int. J. Neural Syst. 26, 1650013 (16 pages).CrossrefPubMedGoogle Scholar

  • Balafar, M.A. (2011). Spatial based expectation maximizing (EM). Diagn. Pathol. 6, 14 pages. doi: 10.1186/1746-1596-6–103Google Scholar

  • Baumgartner, J., Flesia, A.G., Gimenez, J., and Pucheta, J. (2016). A new image segmentation framework based on two-dimensional hidden Markov models. Integr. Comput. Aided Eng. 23, 1–13.Google Scholar

  • Benaichouche, N., Oulhadj, H., and Siarry, P. (2013). Improved spatial Fuzzy C-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digital Signal Process. 23, 1390–1400.CrossrefGoogle Scholar

  • Bezdek, J.C. (1999). Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (Boston, USA: Springer US).Google Scholar

  • Cabria, I. and Gondra, I. (2015). Automated localization of brain tumors in MRI using potential-K-means clustering algorithm. 2015 IEEE Conference on Computer and Robot Vision, Halifax, NS, Canada, pp. 125–132.Google Scholar

  • Cadenas, J.M., Garrido, M.C., and Martínez, R. (2013). Feature subset selection filter–wrapper based on low quality data. Expert Syst. Appl. 40, 6241–6252.CrossrefGoogle Scholar

  • Castro, L.N. (2006). Fundamental of Natural Computing (FL, USA: Chapman & Hall/CRC).Google Scholar

  • Cha, Y.J., Choi, W., and Buyukozturk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks. Comput. Aided Civ. Inf. Eng. 32, 361–378.CrossrefGoogle Scholar

  • Charron, O., Lallement, A., Janet, D., Noblet, V., Clavier, J.B., and Meyer, P. (2018). Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput. Biol. Med. 95, 43–54.CrossrefPubMedGoogle Scholar

  • Chuzel, P., Mansour, A., Ognard, J., Gentric, J., Bressollette, L., Hamad, D., and Betrouni, N. (2016). Automatic clustering for MRI images, application on perfusion MRI of brain. IEEE 2nd International Conference on Frontiers of Signal Processing, Warsaw, Poland, pp. 63–66.Google Scholar

  • Dai, H. (2017). A wavelet support vector machine-based neural network meta model for structural reliability assessment. Comput. Aided Civ. Infr. Eng. 32, 344–357.CrossrefGoogle Scholar

  • Dale, B.M., Brown, M.A., and Semelka, R.C. (2015). MRI: Basic Principles and Applications (Hoboken, NJ, USA: Wiley-Blackwell), 248 pages.Google Scholar

  • Dorigo, M., Birattari, M., and Stutzle, T. (2006). Ant colony optimization. IEEE Comput. Intell. Mag. 4, 28–39.Google Scholar

  • Duda, R.O., Hart, P.E., and Stork, D.G. (2012). Pattern Classification (Hoboken, NJ, USA: Wiley-Interscience), 680 pages.Google Scholar

  • Dvorak, P. and Menze, B.H. (2015). Structured prediction with convolutional neural networks for multimodal brain tumor segmentation. Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge, pp. 13–24.Google Scholar

  • Ester, M., Kriegel, H., Sander, J., and Xu, X. (1996). A density based algorithm for discovering clusters in large spatial databases with noise. KDD’96 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, pp. 226–231.Google Scholar

  • Forcael, E., González, V., Orozco, F., Vargas, S., Moscoso, P., Pantoja, A., and Moscoso, P. (2014). Ant colony optimization model for tsunamis evacuation routes. Comput. Aided Civ. Inf. Eng. 29, 723–737.CrossrefGoogle Scholar

  • Gao, Z.-K., Cai, Q., Yang, Y.-X., Dong, N., and Zhang, S.-S. (2017). Visibility graph from adaptive optimal-kernel time-frequency representation for classification of epileptiform EEG. Int. J. Neural Syst. 27, 1750005 (12 pages).CrossrefPubMedGoogle Scholar

  • Ghosh-Dastidar, S. and Adeli, H. (2003). Wavelet-clustering-neural network model for freeway incident detection. Comput. Aided Civ. Inf. Eng. 18, 325–338.CrossrefGoogle Scholar

  • Ghosh-Dastidar, S., Adeli, H., and Dadmehr, N. (2006). Voxel-based morphometry in Alzheimer’s patients. J. Alzheimers Dis. 10, 445–447.PubMedCrossrefGoogle Scholar

  • Gors, D., Suetens, P., Vandenberghe, R., and Claes, P. (2017). Hierarchical spectral clustering of MRI for global-to-local shape analysis: applied to brain variations in Alzheimer’s disease. IEEE International Symposium on Biomedical Imaging, April 2017, Melbourne, Australia, pp. 787–791.Google Scholar

  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018). Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377.CrossrefGoogle Scholar

  • Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Yoshua, B., Pal, C., Jodin, P.M., and Larochelle, H. (2017). Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31.CrossrefPubMedGoogle Scholar

  • He, K., Zhang, X., Sun, J. (2015). Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, pp. 1026–1034.Google Scholar

  • Hinnebur, A. and Keim, D.A. (1998). An efficient approach to clustering in large multimedia databases with noise. KDD’98 Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 58–65.Google Scholar

  • Hoo-Chang, S., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., and Summers, R.M. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35, 1285–1298.PubMedCrossrefGoogle Scholar

  • Hung, S.L. and Adeli, H. (1993). Parallel backpropagation learning algorithms on Cray Y-MP8/864 Supercomputer. Neurocomputing 5, 287–302.CrossrefGoogle Scholar

  • Hussain, S, Anwar, S.M., and Majid, M. (2018). Segmentation of glioma tumors in brain using deep convolutional neural network. Neurocomputing 282, 248–261.CrossrefGoogle Scholar

  • Irani, J., Pise, N., and Phatak, M. (2016). Clustering techniques and the similarity measures used in clustering: a survey. Int. J. Comput. Appl. 134, 9–14.Google Scholar

  • Jain, A.K. (2010). Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666.CrossrefGoogle Scholar

  • Ji, Z., Liu, J., Cao, G., Sun, Q., and Qiang, C. (2014). Robust spatially constrained Fuzzy C-means algorithm for brain MR image segmentation. Pattern Recognit. 47, 2454–2466.CrossrefGoogle Scholar

  • Jiang, K.Z., Zhen, Z.T., and Li, L. (2017). Topological structure matching measure between two graphs. Comput. Aided Civ. Inf. Eng. 32, 515–524.CrossrefGoogle Scholar

  • Jovic, A., Brkic, K., and Bogunovic, N. (2015). A review of feature selection methods with applications. IEEE International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, May 2015, pp. 1200–1205.Google Scholar

  • Kamnitsas, K., Leding, C., Newcombe, V.F.J., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., and Glocker, B. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78.CrossrefPubMedGoogle Scholar

  • Khaliluzzaman, M., Dolon, L.I., and Deb, K. (2016). Analyzing MRI segmentation based on wavelet and BEMD using Fuzzy C-means clustering. 2016 International Workshop on Computational Intelligence (IWCI), Bangladesh, pp. 15–20.Google Scholar

  • Khedher, L., Illan, I.A., Gorriz, J.M., Ramirez, J., Brahim, A., and Meyer-Baese, A. (2017). Independent component analysis-support vector machine-based computer-aided diagnosis system for Alzheimer’s with visual support. Int. J. Neural Syst. 27, 1650050 (18 pages).PubMedCrossrefGoogle Scholar

  • Kim, Y.H. and Peeta, S. (2016). Graph-based modeling of information flow evolution and propagation under V2V communications based advanced traveler information systems. Comput. Aided Civ. Inf. Eng. 31, 499–514.CrossrefGoogle Scholar

  • Kinani, J.M.V., Silva, A.J.R., Funes, F.J.G., and Arellano, A. (2014). Fuzzy C-means applied to MRI images for an automatic lesion detection using image enhancement and constrained clustering. 2014 IEEE 4th International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, pp. 1–7.Google Scholar

  • Kotler, P. and Keller, K.L. (2009). Marketing Management, 13th ed. (Upper Saddle River, NJ, USA: Prentice Hall (Pearson)).Google Scholar

  • Koziarski, M. and Cyganek, B. (2017). Image recognition with deep neural networks in presence of noise – dealing with and taking advantage of distortions. Integr. Comput. Aided Eng. 24, 337–350.CrossrefGoogle Scholar

  • Kulkarni, A., Tokekar, V., and Kulkarni, P. (2015). Discovering context of labelled text documents using context similarity coefficient. Procedia Comput. Sci. 49, 118–127.CrossrefGoogle Scholar

  • Lebenberg, J., Poupon, C., Thirion, B., Leroy, F., Mangin, J.F., Lambertz, G., and Dubois, J. (2015). Clustering the infant brain tissues based on microstructural properties and maturation assessment using multi-parametric, 2015 IEEE 12th International Symposium on Biomedical Imaging, New York, USA, pp. 148–151.Google Scholar

  • LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nat. Int. J. Sci. 521, 436–444.Google Scholar

  • Lin, Y.Z., Nie, Z.H., and Ma, H.W. (2017). Structural damage detection with automatic feature-extraction through deep learning. Comput. Aided Civ. Inf. Eng. 32, 1025–1046.CrossrefGoogle Scholar

  • Liu, J. and Guo, L. (2015a). A new brain MRI image segmentation strategy based on wavelet transform and k-means clustering. IEEE International Conference on Signal Processing, Communications and Computing, Ningbo, China, pp. 1–4.Google Scholar

  • Liu, J.W. and Guo, L. (2015b). Selection of initial parameters of K-means clustering algorithm for MRI brain image segmentation. 2015 IEEE International Conference on Machine Learning and Cybernetics (ICMLC), Guangzhou, China, pp. 123–127.Google Scholar

  • Liu, R. Wang, Y., Newman, G.I., Ying, S., and Thakor, N.V. (2017). EEG classification with a sequential decision-making method in motor imagery BCI. Int. J. Neural. Syst. 27, 1750046.CrossrefPubMedGoogle Scholar

  • Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J.C., Quiles, A., Valls, L., Torrenta, L.R., and Rovira, A. (2012). Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf. Sci. 186, 164–185.CrossrefGoogle Scholar

  • Luxburg, U.V. (2006). A tutorial on spectral clustering. Technical Report, No. TR-149.Google Scholar

  • Ma, M., Liang, J., Guo, M., Fan, Y., and Yin, Y. (2011). SAR image segmentation based on artificial bee colony algorithm. Appl. Soft Comput. 11, 5205–5214.CrossrefGoogle Scholar

  • Makropoulos, A., Gousias, I.S., Ledig, C., Aljabar, P., Serag, A., Hajnal, J.V., Edwards, A.D., and Counsell, S.J. (2014). Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans. Med. Imaging 33, 1818–1831.PubMedCrossrefGoogle Scholar

  • Mbuyamba, E.I., Duarte, J.M.C., Cervantes, J.G.A., Correa-Cely, C.R., Linder, D., and Chalopin, C. (2016). Active contours driven by cuckoo search strategy for brain tumor images segmentation. Expert Syst. Appl. 56, 59–68.CrossrefGoogle Scholar

  • Mekhmoukh, A. and Makrani, K. (2015). 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. 122, 266–281.PubMedCrossrefGoogle Scholar

  • Menon, N. and Ramakrishnan, R. (2015). Brain tumor segmentation in MRI images using unsupervised artificial bee colony algorithm and FCM clustering. 2015 IEEE International Conference on Communications and Signal Processing, Melmaruvathur, India, pp. 6–9.Google Scholar

  • Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–20124.CrossrefPubMedGoogle Scholar

  • Milletari, F., Ahmadi, S.A., Kroll, C., Hennersperger, C., Tombari, F., Shah, A., Plate, A., Boetzel, K., and Nassir, N. (2015). Robust segmentation of various anatomies in 3D ultrasound using Hough forests and learned data representations. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Munich, Germany, pp. 111–118.Google Scholar

  • Milletari, F., Ahmadi, S.A., Kroll, C., Plate, A., Rozanski, V., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B., Bötzel, K., et al. (2017). Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Understand. 164, 92–102.CrossrefGoogle Scholar

  • Mirzaei, G. and Adeli, H. (2016). Resting state functional magnetic resonance imaging processing techniques in stroke studies. Rev. Neurosci. 27, 871–885.PubMedGoogle Scholar

  • Mirzaei, G., Majid, M.W., Ross, J., Jamali, M.M., Gorsevski, P.V., Frizado, J., and Bingman, V.P. (2012a). Implementation of ant clustering algorithm for IR imagery in wind turbine applications. 2012 IEEE 55th International Midwest Symposium on Circuits and Systems (MWSCAS), Boise, ID, USA, pp. 868–871.Google Scholar

  • Mirzaei, G., Majid, M.W., Ross, J., Jamali, M.M., Gorsevski, P.V., Frizado, J., and Bingman, V.P. (2012b). The bio-acoustic feature extraction and classification of bat echolocation calls. 2012 IEEE International Conference on Electro/Information Technology, Indianapolis, IN, USA, pp. 1–4.Google Scholar

  • Mirzaei, G., Jamali, M.M., Ross, J., Gorsevski, P.V., and Bingman, V.P. (2015). Data fusion of acoustics, infrared, and marine radar. IEEE Sens. J. 15, 6625–6632.CrossrefGoogle Scholar

  • Mirzaei, G., Adeli, A., and Adeli, H. (2016). Imaging and machine learning techniques for diagnosis of Alzheimer’s disease. Rev. Neurosci. 27, 857–870.PubMedGoogle Scholar

  • Morabito, F.C., Campolo, M., Mammone, N., Versaci, M., Franceschetti, S., Tagliavini, F., Sofia, V., Fatuzzo, D., Gambardella, A., Labate, A., et al. (2017). Deep learning representation from electroencephalography of early-stage Creutzfeld-Jakob disease and features for differentiation from rapidly progressive dementia. Int. J. Neural. Syst. 27, 1650039 (15 pages).CrossrefGoogle Scholar

  • Muneer, K.V.H. and Joseph, K.P. (2018). Performance analysis of combined K-means and Fuzzy C-means segmentation of MR brain images. Computational Vision Bio Insp. Comp. 28, 830–836.CrossrefGoogle Scholar

  • Nayak, J., Naik, B., and Behera, H.S. (2014). Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014. Comput. Intell. Data Mining 2, 133–149.Google Scholar

  • O’Neill, M. and Brabazon, A. (2006). Grammatical swarm: the generation of programs by social programming. Nat. Comput. 5, 443–462.CrossrefGoogle Scholar

  • Ortega-Zamorano, F., Jerez, J.M., Gómez, I., and Franco, L. (2017). Layer multiplexing FPGA implementation for deep back-propagation learning. Integr. Comput. Aided Eng. 24, 171–185.CrossrefGoogle Scholar

  • Ortiz-Garcia, A., Munilla, J., Gorriz, J.M., and Ramirez, J. (2016). Ensembles of deep learning architectures for the early diagnosis of Alzheimer’s disease. Int. J. Neural Syst. 26, 1650025 (23 pages).PubMedCrossrefGoogle Scholar

  • Palomo, E.J. and Lopez-Rubio, E. (2016). Learning topologies with the growing neural forest. Int. J. Neural Syst. 26, 1650019 (21 pages).PubMedCrossrefGoogle Scholar

  • Peng, H., Wang, J., Shi, P., Perez-Jimenez, M.J., and Riscos-Nunez, A. (2016). An extended membrane system with active membranes to solve automatic fuzzy clustering problems. Int. J. Neural Syst. 26, 1650004 (17 pages).CrossrefPubMedGoogle Scholar

  • Pereira, S., Pinto, A., Alves, V., and Silva, C. (2016). Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans. Med. Imaging 35, 1240–1251.CrossrefPubMedGoogle Scholar

  • Prakash, R.M. and Kumari, R.S.S. (2017). Spatial Fuzzy C means and expectation maximization algorithms with bias correction for segmentation of MR brain images. J. Med. Syst. 41, 9 pages. doi: 10.1007/s10916-016-0662-7.PubMedGoogle Scholar

  • Qiu, B.Z., Li, X.L., and Shen, J.Y. (2007). Grid-based clustering algorithm based on intersecting partition and density estimation. T. Washio, eds. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science, 4819 (Berlin, Heidelberg: Springer).Google Scholar

  • Rafiei, M.H. and Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Construct. Eng. Manage. 142, 04015066, doi: 10.1061/(ASCE)Co.1943-7862.000104.CrossrefGoogle Scholar

  • Rafiei, M.H. and Adeli, H. (2017). A new neural dynamic classification algorithm. IEEE Trans. Neural Netw. Learn. Syst. 28, 3074–3083.PubMedCrossrefGoogle Scholar

  • Rafiei, M.H. and Adeli, H. (2018). A novel unsupervised deep learning model for global and local health condition assessment of structures. Eng. Struct. 156, 598–607.CrossrefGoogle Scholar

  • Rani, U. and Sahu, S. (2017). Comparison of clustering techniques for measuring similarity in articles. 3rd IEEE International Conference on Computational Intelligence and Communication Technology, Ghaziabad, India, pp. 1–7.Google Scholar

  • Rashidi, T.H., Rey, D., Jian, S., and Waller, S.T. (2016). A clustering algorithm for bi-criteria stop location design with elastic demand. Comput. Aided Civ. Inf. Eng. 31, 117–131.CrossrefGoogle Scholar

  • Rigos, A., Tsekouras, G.E., Vousdoukas, M.I., Chatzipavlis, A., and Velegrakis, A.F. (2016). A Chebyshev polynomial radial basis function neural network for automated shoreline extraction from coastal imagery. Integr. Comput. Aided Eng. 23, 141–160.CrossrefGoogle Scholar

  • Saignavongs, M., Ciumas, C., Petton, M., Bouet, R., Boulogne, S., Rheims, S., Lachaux, J.-P., and Ryvlin P. (2017). Neural activity elicited by a cognitive task can be detected in single-trials with simultaneous intracerebral EEG-fMRI recordings. Int. J. Neural Syst. 27, 1750001 (14 pages).CrossrefPubMedGoogle Scholar

  • Samant, A. and Adeli, H. (2000). Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput. Aided Civ. Inf. Eng. 15, 241–250.CrossrefGoogle Scholar

  • Sandri, M. and Zuccolotto, P. (2006). Variable selection using random forests. Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. S. Zani, A. Cerioli, M. Riani, M. Vichi, eds. (Berlin, Heidelberg: Springer).Google Scholar

  • Sankari, Z. and Adeli, H. (2011). Probabilistic neural networks for diagnosis of Alzheimer’s disease using conventional and wavelet coherence. J. Neurosci. Methods 197, 165–170.CrossrefPubMedGoogle Scholar

  • Sarafrazi, S. and Nezamabadi-pour, H. (2013). Facing the classification of binary problems with a GSA-SVM hybrid system. Math. Comput. Modell. 57, 270–278.CrossrefGoogle Scholar

  • Sharma, M., Purohit, G.N., and Mukherjee, S. (2018). Information retrieves from brain MRI images for tumor detection using hybrid technique K-means and artificial neural network (KMANN). Networking Communication and Data Knowledge Engineering, Vol. 4 (Berlin, Heidelberg: Springer), pp. 145–157.Google Scholar

  • Shirkhorshidi, A.S., Aghabozorgi, S., and Wah, T.Y. (2015). A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS ONE 10, e0144059, doi: 10.1371/journal.pone.0144059.PubMedCrossrefGoogle Scholar

  • Siddique, N. and Adeli, H. (2013). Computational intelligence – synergies of fuzzy logic. Neural Netw. Evol. Comput. (West Sussex, UK: Wiley).Google Scholar

  • Siddique, N. and Adeli, H. (2017). Nature Inspired Computing – Physics- and Chemistry-based Algorithms (Boca Raton, FL: CRC Press, Taylor & Francis).Google Scholar

  • Siddique, N.H. and Adeli, H. (2015). Nature-inspired computing: an overview and some future directions. Cogn. Comput. 7, 706–714.CrossrefGoogle Scholar

  • Siddique, N.H. and Adeli, H. (2016a). Brief history of natural sciences for nature-inspired computing in engineering. J. Civ. Eng. Manage. 22, 287–301.CrossrefGoogle Scholar

  • Siddique, N.H. and Adeli, H. (2016b). Physics-based search and optimization: inspirations from nature. Expert Syst. 33, 607–623.CrossrefGoogle Scholar

  • Singh, N., Fletcher, P.T., Preston, J.S., King, R.D., Marron, J.S., Weiner, M.W., Joshi, S., and Alzheimer’s Disease Neuroimaging Initiative (ADNI). (2014). Quantifying anatomical shape variations in neurological disorders. Med. Image Anal. 18, 616–633.CrossrefPubMedGoogle Scholar

  • Tonks, D.G. (2009). Validity and the design of market segments. J. Market. Manage. 25, 341–356.CrossrefGoogle Scholar

  • Vaishnavee, K.B. and Amshakala, K. (2015). An automated MRI brain image segmentation and tumor detection using SOM-clustering and proximal support vector machine classifier. 2015 IEEE International Conference on Engineering and Technology, Coimbatore, India, pp. 1–6.Google Scholar

  • Von Luxburg, U. (2007). A tutorial on spectral clustering. Stat. Comput. 17, 395–416.CrossrefGoogle Scholar

  • Vora, A. and Raman, S. (2018). Iterative spectral clustering for unsupervised object localization. Pattern Recognit. Lett. 106, 27–32.CrossrefGoogle Scholar

  • Wang, W., Yang, J., and Muntz, R.R. (1997). STING: a statistical information grid approach to spatial data mining. VLDB 97, Proceeding of 23rd International Conference on Very large Data Bases, Morgan Kaufmann, pp. 186–195.Google Scholar

  • Wang, R., Zhang, Y., and Zhang, L. (2016). An adaptive neural network approach for operator functional state prediction using psychophysiological data. Integr. Comput. Aided Eng. 23, 81–97.Google Scholar

  • Washimkar, S.P. and Chede, S.D. (2015). Application of FCM clustering on AM-FM to detect MRI disease progression for multiple sclerosis. International Conference on Computational Intelligence and Commutation Networks, December 2015, Jabalpur, India, pp. 283–287.Google Scholar

  • Witten, I.H., Frank, E., and Hall, M. (2011). Data Mining: Practical Machine Learning Tools and Techniques (San Francisco, CA, USA: Morgan Kaufmann).Google Scholar

  • Xiaoyun, C., Yi, C., Xiaoli, Q., Min, Y., and Yanshan, H. (2009). PGMCLU: a novel parallel grid-based clustering algorithm for multi-density datasets. IEEE Symposium on Web Society, Lanzhou, China, pp. 166–171.Google Scholar

  • Xu, R. and Wunsch, D.C. (2010). Clustering algorithms in biomedical research: a review. IEEE Rev. Biomed. Eng. 3, 120–154.CrossrefPubMedGoogle Scholar

  • Xu, F., Zhou, W., Zhen, Y., Yuan, Q., and Wu, Q. (2016). Using fractal and local binary pattern features for motor imagery classification of ECOG motor imagery task obtained from the right brain hemisphere. Int. J. Neural Syst. 26, 1650022 (13 pages).CrossrefGoogle Scholar

  • Yoo, Y., Tang, L.Y.W., Brosch, T., Li, D.K.B., Kolind, S., Vavasour, I., Rauscher, A., Mackay, A.L., Traboulsee, A., and Tam, R.C. (2018). Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. NeuroImage Clin. 17, 69–178.Google Scholar

  • Zeinalia, Y. and Story, B. (2017). Competitive probabilistic neural network. Integrated Comput. Aided Eng. 24, 105–118.CrossrefGoogle Scholar

  • Zhang, D.Q. and Chen, S.C. (2004). A novel kernelized Fuzzy C-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32, 37–50.CrossrefPubMedGoogle Scholar

  • Zhang, A., Wang, K.C.P., Li, B., Yang, E., Dai, X., Peng, Y., Fei, Y., Liu, Y., Li, J.Q., and Chen, C. (2017a). Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput. Aided Civ. Inf. Eng. 32, 805–819.CrossrefGoogle Scholar

  • Zhang, Y., Wang, Y., Jin, J., and Wang, X. (2017b). Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int. J. Neural Syst. 27, 1650032.CrossrefGoogle Scholar

  • Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., and Fan, Y. (2018). A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 43, 98–111.PubMedCrossrefGoogle Scholar

About the article

Received: 2018-05-22

Accepted: 2018-07-19

Published Online: 2018-09-27

Published in Print: 2018-12-19


Citation Information: Reviews in the Neurosciences, Volume 30, Issue 1, Pages 31–44, ISSN (Online) 2191-0200, ISSN (Print) 0334-1763, DOI: https://doi.org/10.1515/revneuro-2018-0050.

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