Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers

Nihat Adar, Savaş Okyay, Kemal Özkan, Suzan Şaylısoy, Belgin Demet Özbabalık Adapınar, Baki Adapınar
  • Savaş Okyay
    Affiliation not present
  • Kemal Özkan
    Affiliation not present
  • Suzan Şaylısoy
    Affiliation not present
  • Belgin Demet Özbabalık Adapınar
    Affiliation not present
  • Baki Adapınar
    Affiliation not present

Abstract

Dementias are termed as neuropsychiatric disorders. Brain images of dementia patients can be obtained through magnetic resonance imaging systems. The relevant disease can be diagnosed by examining critical regions of those images. Certain brain characteristics such as the cortical volume, the thickness, and the surface area may vary among dementia types. These attributes can be expressed as numerical values using image processing techniques. In this study, the dataset involves T1 medical image sets of 63 samples. Each particular sample is labeled with one of the three dementia types: Alzheimer's disease, frontotemporal dementia, and vascular dementia. The image sets are processed to create different feature groups. These are cortical volumes, gray volumes, surface areas, and thickness averages. The main objective is seeking brain sections more effective in establishing the clinical diagnosis. In other words, searching an optimal feature subset process is carried out for each feature group. To that end, a wrapper feature selection technique namely genetic algorithm is used with Naive Bayes classifier and support vector machines. The test phase is performed by using 10-fold cross validation. Consequently, accuracy results up to 93.7% with different classifiers and feature selection parameters are shown.

Anahtar Kelimeler

Keywords

Genetic algorithm; Feature selection;Dementia;Classification;Magnetic resonance imaging

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Submitted: 2018-12-21 10:45:25
Published: 2016-12-26 00:00:00
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References

C. Güngen, T. Ertan, E. Eker, R. Yaşar and F. Engin (2002). Validity and reliability study on standardized mini mental test for the diagnosis of mild dementia in the Turkish society. Turkish Journal of Psychiatry. Vol.13. Pages. 273–281.

H.G. Kreeftenberg, E.L. Mooyaart, J.R. Huizenga and W.J. Sluiter (2000). Quantification of liver iron concentration with magnetic resonance imaging by combining T1-, T2-weighted spin echo sequences and a gradient echo sequence. Neth. J. Med. Vol.56. Pages. 133–137.

E. Westman, A. Simmons, J.S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, M.W. Weiner, S. Lovestone, C. Spenger and L.O. Wahlund (2011). AddNeuroMed and ADNI: similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage. Vol.58. Pages. 818–828.

R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M.O. Habert, M. Chupin, H. Benali, O. Colliot, ADNI and others (2011). Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. Vol.56. Pages. 766–781.

J. Escudero, J.P. Zajicek and E. Ifeachor (2011). Machine Learning classification of MRI features of Alzheimer’s disease and mild cognitive impairment subjects to reduce the sample size in clinical trials. in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, Pages. 7957–7960.

W.B. Jung, Y.M. Lee, Y.H. Kim and C.W. Mun (2015). Automated classification to predict the progression of Alzheimer’s disease using whole-brain volumetry and DTI. Psychiatry Investig. Vol.12. Pages. 92–102.

C. Aguilar, E. Westman, J.S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, L. Kloszewska, H. Soininen, S. Lovestone, C. Spenger and others (2013). Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment. Psychiatry Res. Neuroimaging. Vol.212. Pages. 89–98.

B. Fischl (2012). FreeSurfer. Neuroimage. Vol.62. Pages. 774–781.

A.M. Dale, B. Fischl and M.I. Sereno (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage. Vol.9. Pages. 179–194.

B. Fischl, A. Liu and A.M. Dale (2001). Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. Med. Imaging, IEEE Trans. Vol.20. Pages. 70–80.

E.H.B.M. Gronenschild, P. Habets, H.I.L. Jacobs, R. Mengelers, N. Rozendaal, J. Van Os and M. Marcelis (2012). The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS One. Vol.7. Pages. e38234.

I. Rish (2001). An empirical study of the naive Bayes classifier. in IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol.3. Pages. 41–46.

C.J.C. Burges (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. Vol.2. Pages. 121–167.

M. Pei, E.D. Goodman, W.F. Punch and Y. Ding (1995). Genetic algorithms for classification and feature extraction. in Classification Society Conference.

A. Konak, D.W. Coit and A.E. Smith (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliab. Eng. Syst. Saf. Vol.91. Pages. 992–1007.

R.S. Sexton, R.E. Dorsey and J.D. Johnson (1999). Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing. Eur. J. Oper. Res. Vol.114. Pages. 589–601.

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