Güzin ÖZMEN, Seral ÖZŞEN


The functional MR images consist of very high dimensional data containing thousands of voxels, even for a single subject. Data reduction methods are inevitable for the classification of these three-dimensional images. In this study in the first step of the data reduction, the first level statistical analysis was applied to fMRI data and brain maps of each subject were obtained for the feature extraction. In the second step the feature selection was applied to brain maps. According to the feature selection method used in the classification studies of fMRI and which is called as the active method, the intensity values of all brain voxels are ranked from high to low and some of these features are presented to the classifier. However, the location information of the voxels is lost with this method. In this study, a new feature extraction method was presented for use in the classification of fMRI. According to this method, active voxels can be used as features by considering brain maps obtained in three dimensions as slice based. Since the functional MR images have big data sets, the selected features were once again reduced by Principal Component Analysis and the voxel intensity values were presented to the classifiers. As a result; 83.9% classification accuracy was obtained by using kNN classifier with purposed slice-based feature extraction method and it was seen that the slice-based feature extraction method increased the classification.


Classification, Feature extraction, fMRI, SPM

Full Text:



Billor N, Godwin J. Variable Selection for Functional Logistic Regression in fMRI Data Analysis.Turkiye Klinikleri Journal of Biostatistics 2015; vol. 7, no. 1

Chen JE, Glover GH. Functional magnetic resonance imaging methods.Neuropsychology review 2015; vol. 25, no. 3, pp. 289-313.

Friston K, Ashburner J, Frith CD, Poline JB, Heather JD, Frackowiak RS. Spatial registration and normalization of images. Human brain mapping 1995; vol. 3, no. 3, pp. 165-189.

Francis S, Panchuelo RS. Physiological measurements using ultra-high field fMRI: a review. Physiological measurement 2014; vol. 35, no. 9, p. R167.

Myers RH, Montgomery DC. A tutorial on generalized linear models. Journal of Quality Technology 1997; vol. 29, no. 3, p. 274.

Friston KJ, Holmes AP,Worsley KJ, Poline JP, Frith CD, Frackowiak ,RS. Statistical parametric maps in functional imaging: a general linear approach. Human brain mapping 1994; vol. 2, no. 4, pp. 189-210.

Mitchell TM. Learning to decode cognitive states from brain images. Machine learning 2004; vol. 57, no. 1-2, pp. 145-175.

Suma H, Murali S. Principal Component Analysis for Analysis and Classification of fMRI Activation Maps. International journal of computer science and network security 2007; vol. 7, no. 11, pp. 235-242.

Mourão-Miranda J. Patient classification as an outlier detection problem: an application of the one-class support vector machine. Neuroimage 2011; vol. 58, no. 3, pp. 793-804.

Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Frontiers in psychiatry 2015, vol. 6, p. 21.

Misaki M, Kim Y, Bandettini PA, Kriegeskorte N. Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage2010; vol. 53, no. 1, pp. 103-118.

Kuncheva LI and Rodríguez JJ. Classifier ensembles for fMRI data analysis: an experiment. Magnetic resonance imaging 2010; vol. 28, no. 4, pp. 583-593.

Tripoliti EE, Fotiadis DI, Argyropoulou M, Manis G. A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data. Journal of biomedical informatics 2010; vol. 43, no. 2, pp. 307-320.

ÖZMEN G, Fonksiyonel Mr Görüntülerini Filtrelemede Yeni Bir Yaklaşim Ve Depresyon Hastalarinin Siniflandirilmasi Üzerine Etkileri, PhD. thesis,, Selcuk University, Graduate School of Natural and Applied Sciences 2018.

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Selcuk University Journal of Engineering Sciences (SUJES) ISSN:2757-8828

Abstracting and indexing

Index Copernicus International


Selcuk university journal of engineering sciences (Online)

ICI World of Journals


Eurasian Scientific Journal Index