Feature Selection from 3D Brain Model for Some Dementia Subtypes Using Genetic Algorithm

  • Savas Okyay Eskişehir Osmangazi University
  • Nihat Adar Eskişehir Osmangazi University
  • Kemal Ozkan Eskişehir Osmangazi University
  • Baki Adapinar Eskişehir Osmangazi University
Keywords: 3D brain model, Dementia subtypes, Feature selection, Genetic algorithm, Magnetic resonance imaging

Abstract

Brain scans that are appropriate to the medical standards are obtained from magnetic resonance imaging devices. Through image processing techniques, 3D brain models can be constructed by mapping medical brain imaging files structurally. Physical characteristics of patient brains can be extracted from those 3D brain models. Characteristics of some specific brain regions are more efficacious in predicting the type of the disease. For that reason, researches are made for finding the worthwhile features out using cortical volumes, gray volumes, surface areas, and thickness averages for left and right brain parts separately or together. The main objective of this work is determining more influential sections throughout the entire brain in establishing the clinical diagnosis. To that end, among all the measurements exported from 3D models, the significant brain features that are effective in identifying some dementia subtypes are sought. The dataset has 3D brain models generated from magnetic resonance scans of 63 samples. Each sample is labeled with one of the following three disease types: Alzheimer’s disease (19), frontotemporal dementia (19), and vascular dementia (25). The genetic algorithm based wrapper feature selection method with various classifiers is proposed to select the features that state the aforementioned dementia subtypes best. The tests are performed by applying cross validation technique and confusion matrices are shown. At the end, the best features are listed, and the accuracy results up to 95.2% are achieved.

Downloads

Download data is not yet available.

Author Biographies

Savas Okyay, Eskişehir Osmangazi University
Department of Computer EngineeringResearch Assistant
Nihat Adar, Eskişehir Osmangazi University
Department of Computer EngineeringAssistant Professor
Kemal Ozkan, Eskişehir Osmangazi University
Department of Computer EngineeringAssociate Professor
Baki Adapinar, Eskişehir Osmangazi University
Department of RadiologyProfessor

References

C. Güngen, T. Ertan, E. Eker, R. Yaşar, and F. Engin, “Standardize mini mental test’in Türk toplumunda hafif demans tanısında geçerlik ve güvenilirliği,” Türk Psikiyatr. Derg., vol. 13, no. 4, pp. 273–281, 2002.

A. Wimo, B. Winblad, H. Aguero-Torres, and E. von Strauss, “The magnitude of dementia occurrence in the world,” Alzheimer Dis. Assoc. Disord., vol. 17, no. 2, pp. 63–67, 2003.

D. Herek and N. Karabulut, “Manyeti̇k rezonans görüntüleme,” TTD Toraks Cerrahisi Bülteni, vol. 1, no. 3, pp. 214–222, 2010.

M. Sebban and R. Nock, “A hybrid filter/wrapper approach of feature selection using information theory,” Pattern Recognit., vol. 35, no. 4, pp. 835–846, 2002.

R. Cuingnet, E. Gerardin, J. Tessieras, G. Auzias, S. Lehéricy, M. O. Habert, M. Chupin, H. Benali, O. Colliot, A. D. N. Initiative, and others, “Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database,” Neuroimage, vol. 56, no. 2, pp. 766–781, 2011.

J. Escudero, J. P. Zajicek, and E. Ifeachor, “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, 2011, pp. 7957–7960.

C. Aguilar, E. Westman, J. S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, S. Lovestone, C. Spenger, and others, “Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment,” Psychiatry Res. Neuroimaging, vol. 212, no. 2, pp. 89–98, 2013.

W. B. Jung, Y. M. Lee, Y. H. Kim, and C. W. Mun, “Automated classification to predict the progression of Alzheimer’s disease using whole-brain volumetry and DTI,” Psychiatry Investig., vol. 12, no. 1, pp. 92–102, 2015.

Q. Zhou, M. Goryawala, M. Cabrerizo, J. Wang, W. Barker, D. A. Loewenstein, R. Duara, and M. Adjouadi, “An optimal decisional space for the classification of Alzheimer’s disease and mild cognitive impairment,” IEEE Trans. Biomed. Eng., vol. 61, no. 8, pp. 2245–2253, 2014.

S. Okyay, N. Adar, K. Özkan, S. Şayslısoy, D. B. Adapınar, and B. Adapınar, “Classification of some dementia types due to feature selection with artificial neural networks,” in IEEE 24th SIU, 2016.

N. Adar, S. Okyay, K. Özkan, S. Şaylısoy, B. D. Özbabalık Adapınar, and B. Adapınar, “Feature Selection on MR Images Using Genetic Algorithm with SVM and Naive Bayes Classifiers,” Int. J. Intell. Syst. Appl. Eng., vol. 4, pp. 170–174, 2016.

B. Fischl, “FreeSurfer,” Neuroimage, vol. 62, no. 2, pp. 774–781, 2012.

M. Reuter, N. J. Schmansky, H. D. Rosas, and B. Fischl, “Within-subject template estimation for unbiased longitudinal image analysis,” Neuroimage, vol. 61, no. 4, pp. 1402–1418, 2012.

A. M. Dale, B. Fischl, and M. I. Sereno, “Cortical surface-based analysis: I. Segmentation and surface reconstruction,” Neuroimage, vol. 9, no. 2, pp. 179–194, 1999.

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

I. Rish, “An empirical study of the naive Bayes classifier,” in IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001, vol. 3, no. 22, pp. 41–46.

C. C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discov., vol. 2, no. 2, pp. 121–167, 1998.

E. Westman, A. Simmons, J. S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kłoszewska, H. Soininen, M. W. Weiner, S. Lovestone, and others, “AddNeuroMed and ADNI: similar patterns of Alzheimer’s atrophy and automated MRI classification accuracy in Europe and North America,” Neuroimage, vol. 58, no. 3, pp. 818–828, 2011.

Published
2017-07-31
How to Cite
[1]
S. Okyay, N. Adar, K. Ozkan, and B. Adapinar, “Feature Selection from 3D Brain Model for Some Dementia Subtypes Using Genetic Algorithm”, IJISAE, pp. 1-6, Jul. 2017.
Section
Research Article