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1. [1] Selvy P, Palanisamy V, Purusothaman T. Performance analysis of clustering algorithms in brain tumor detection of MR images. Eur J Sci Res 2011; 62: 321-330. 2. [2] Saha M, Panda C. A review on various image segmentation techniques for brain tumor detection. Int Confer Electron Commun Aerospace Technol 2018. 3. [3] Mayer AR, Bellgowan PS, Hanlon FM. Functional magnetic resonance imaging of mild traumatic brain injury. Neurosci Biobehav Rev 2015; 49: 8-18. [ DOI:10.1016/j.neubiorev.2014.11.016] [ PMID] 4. [4] Anuradha R, Sushila R, Pranav M. Performance analysis of K-means and CLARANS clustering algorithm. Int J Manag Technol Engin 2018; 8: 374-378. 5. [5] Filler A. The history, development and impact of computed imaging in neurological diagnosis and neurosurgery: CT, MRI, and DTI. Int J Neurosurg 2010; 7: 5-35. [ DOI:10.5580/23c6] 6. [6] Lindquist M. The statistical analysis of fMRI data. Stat Sci 2008; 23: 439-464. [ DOI:10.1214/09-STS282] 7. [7] Kantardzic M. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons 2011. [ DOI:10.1002/9781118029145] [ PMID] 8. [8] Zhang T, Ramakrishnan R, Livny M. BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Record 1996. [ DOI:10.1145/233269.233324] 9. [9] Madan S, Dana KJ. Modified balanced iterative reducing and clustering using hierarchies (m-BIRCH) for visual clustering. Pattern Anal Appl 2016; 19: 1023-1040. [ DOI:10.1007/s10044-015-0472-4] 10. [10] www.nitrc.org. last visit: 15 May 2019. 11. [11] Windischberger C, Barth M, Lamm C, Schroeder L, Bauer H, Gur RC, Moser E. Fuzzy cluster analysis of high-field functional MRI data. Artif Intell Med 2003; 29: 203-223. [ DOI:10.1016/S0933-3657(02)00072-6] 12. [12] Heller R, Stanley D, Yekutieli D, Benjamini Y. Cluster-based analysis of FMRI data. NeuroImage 2006; 33: 599-608. [ DOI:10.1016/j.neuroimage.2006.04.233] [ PMID] 13. [13] Bednarik L, Kovacs L. Efficiency analysis of quality threshold clustering algorithms. Produc Syst Inform Engin 2013; 6: 15-26. 14. [14] Woo CW, Krishnan A, Wager TD. Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage 2014; 91: 412-419. [ DOI:10.1016/j.neuroimage.2013.12.058] [ PMID] [ PMCID] 15. [15] Borumandnia N, Alavi Majd H, Zayeri F, Baghestani A R, Faeghi F, Tabatabaei SM. Bayesian spatiotemporal model for detecting of active areas in brain for analyzing of fMRI data. Koomesh 2017; 19: 845-851. (Persian). 16. [16] zolghadr Z, Alavi Majd H, Faeghi F, Niaghi F, Hajizadeh N. Classification of brain stem glioma tumor grade based on MRI findings using support vector machine. Koomesh 2017; 19: 584-590. (Persian). 17. [1] Selvy P, Palanisamy V, Purusothaman T. Performance analysis of clustering algorithms in brain tumor detection of MR images. Eur J Sci Res 2011; 62: 321-330. 18. [2] Saha M, Panda C. A review on various image segmentation techniques for brain tumor detection. Int Confer Electron Commun Aerospace Technol 2018. 19. [3] Mayer AR, Bellgowan PS, Hanlon FM. Functional magnetic resonance imaging of mild traumatic brain injury. Neurosci Biobehav Rev 2015; 49: 8-18. [ DOI:10.1016/j.neubiorev.2014.11.016] [ PMID] 20. [4] Anuradha R, Sushila R, Pranav M. Performance analysis of K-means and CLARANS clustering algorithm. Int J Manag Technol Engin 2018; 8: 374-378. 21. [5] Filler A. The history, development and impact of computed imaging in neurological diagnosis and neurosurgery: CT, MRI, and DTI. Int J Neurosurg 2010; 7: 5-35. [ DOI:10.5580/23c6] 22. [6] Lindquist M. The statistical analysis of fMRI data. Stat Sci 2008; 23: 439-464. [ DOI:10.1214/09-STS282] 23. [7] Kantardzic M. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons 2011. [ DOI:10.1002/9781118029145] [ PMID] 24. [8] Zhang T, Ramakrishnan R, Livny M. BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Record 1996. [ DOI:10.1145/233269.233324] 25. [9] Madan S, Dana KJ. Modified balanced iterative reducing and clustering using hierarchies (m-BIRCH) for visual clustering. Pattern Anal Appl 2016; 19: 1023-1040. [ DOI:10.1007/s10044-015-0472-4] 26. [10] www.nitrc.org. last visit: 15 May 2019. 27. [11] Windischberger C, Barth M, Lamm C, Schroeder L, Bauer H, Gur RC, Moser E. Fuzzy cluster analysis of high-field functional MRI data. Artif Intell Med 2003; 29: 203-223. [ DOI:10.1016/S0933-3657(02)00072-6] 28. [12] Heller R, Stanley D, Yekutieli D, Benjamini Y. Cluster-based analysis of FMRI data. NeuroImage 2006; 33: 599-608. [ DOI:10.1016/j.neuroimage.2006.04.233] [ PMID] 29. [13] Bednarik L, Kovacs L. Efficiency analysis of quality threshold clustering algorithms. Produc Syst Inform Engin 2013; 6: 15-26. 30. [14] Woo CW, Krishnan A, Wager TD. Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage 2014; 91: 412-419. [ DOI:10.1016/j.neuroimage.2013.12.058] [ PMID] [ PMCID] 31. [15] Borumandnia N, Alavi Majd H, Zayeri F, Baghestani A R, Faeghi F, Tabatabaei SM. Bayesian spatiotemporal model for detecting of active areas in brain for analyzing of fMRI data. Koomesh 2017; 19: 845-851. (Persian). 32. [16] zolghadr Z, Alavi Majd H, Faeghi F, Niaghi F, Hajizadeh N. Classification of brain stem glioma tumor grade based on MRI findings using support vector machine. Koomesh 2017; 19: 584-590. (Persian).
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