Application of modified balanced iterative reducing and clustering using hierarchies algorithm in parceling of brain performance using fMRI data

Navid Valizadeh; Soheila Khodakarim; Seyyed Mohammad Tabatabaei; Azam Saffar; Alireza Akbarzadeh Bagheban

Koomesh

Journal of Semnan University of Medical Sciences

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Application of modified balanced iterative reducing and clustering using hierarchies algorithm in parceling of brain performance using fMRI data

Author(s):

Navid Valizadeh,

Soheila Khodakarim,

Seyyed Mohammad Tabatabaei,

Azam Saffar,

Alireza Akbarzadeh Bagheban

^,*

Koomesh:Vol. 22, issue 4; 644-649

Published online:Dec 06, 2020

Article type:Research Article

Received:Oct 02, 2019

Accepted:Jun 06, 2020

How to Cite:Navid ValizadehSoheila KhodakarimSeyyed Mohammad TabatabaeiAzam SaffarAlireza Akbarzadeh BaghebanApplication of modified balanced iterative reducing and clustering using hierarchies algorithm in parceling of brain performance using fMRI data.koomesh.2020;22(4):e153224.

Abstract

Introduction: Clustering of human brain is a very useful tool for diagnosis, treatment, and tracking of brain tumors. There are several methods in this category in order to do this. In this study, modified balanced iterative reducing and clustering using hierarchies (m-BIRCH) was introduced for brain activation clustering. This algorithm has an appropriate speed and good scalability in dealing with very large data using a new concept of Clustering Feature. Materials and Methods: In this study, data from the brain scan had been used. This dataset consisted of 74 consecutive brain scans. After data preprocessing, brain scan images were clustered through the BIRCH and m-BIRCH algorithms. Data were analyzed using WFU-PickAtlas in Matlab software and were compared with the TD Lobes Standard Atlas. Results: The speed of implementation of the m-BIRCH algorithm decreased as threshold limit increased. The m-BIRCH clustering algorithm showed that there was no specific ascending or descending pattern between branch factor and the run-time of the algorithm. The maximum runtime value of the algorithm was related to the branching factor of 30 which was 94 seconds, equivalent to the upper threshold limit of the BIRCH algorithm. Conclusion: Applying the m-BIRCH algorithm on high-dimension data set such as brain scan images has relative advantages and provides a tradeoff between time and space complexity. By simultaneously increasing the branching factor and threshold limit, the sensitivity of clustering will be decreased

Keywords

Brain

Cluster Analysis

Algorithms

Magnetic Resonance Imaging

مغز

تحلیل خوشه‌ای

الگوریتمها

تصویربرداری از طریق تشدید مغناطیسی

References

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.
Saha M, Panda C. A review on various image segmentation techniques for brain tumor detection. Int Confer Electron Commun Aerospace Technol 2018.
3.
Mayer AR, Bellgowan PS, Hanlon FM. Functional magnetic resonance imaging of mild traumatic brain injury. Neurosci Biobehav Rev 2015; 49: 8-18.
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.
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.
6.
Lindquist M. The statistical analysis of fMRI data. Stat Sci 2008; 23: 439-464.
7.
Kantardzic M. Data mining: concepts, models, methods, and algorithms. John Wiley Sons 2011.
8.
Zhang T, Ramakrishnan R, Livny M. BIRCH: an efficient data clustering method for very large databases. ACM Sigmod Record 1996.
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.
10.
www.nitrc.org. last visit: 15 May 2019.
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.
12.
Heller R, Stanley D, Yekutieli D, Benjamini Y. Cluster-based analysis of FMRI data. NeuroImage 2006; 33: 599-608.
13.
Bednarik L, Kovacs L. Efficiency analysis of quality threshold clustering algorithms. Produc Syst Inform Engin 2013; 6: 15-26.
14.
Woo CW, Krishnan A, Wager TD. Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage 2014; 91: 412-419.
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.
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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