Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-2567
See all formats and pricing
More options …

Texture and Gene Expression Analysis of the MRI Brain in Detection of Alzheimer’s Disease

Alhadi Bustamam
  • Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Kampus UI Depok, Indonesia 16424
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Devvi Sarwinda
  • Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Kampus UI Depok, Indonesia 16424
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Gianinna Ardenaswari
  • Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Kampus UI Depok, Indonesia 16424
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-11-01 | DOI: https://doi.org/10.1515/jaiscr-2018-0008

Abstract

Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

Keywords: Alzheimer’s Disease; MRI; Feature Extraction; Bi-Clustering; Local Binary Pattern (LBP)

References

  • [1] Zhou X, Liu Z, Zhou Z, Xia H: Study on Texture Characteristics of Hippocampus in MR Images of Patients with Alzheimer’s Disease. Proc. 3rd Annu. Conf. Biomedical Engineering and Informatics 2010, Yantai, Beijing.Google Scholar

  • [2] Kassner A and Thornhill R.E: Texture Analysis: A Review of Neurologic MR Imaging Application. American Journal of Neuroradiology 2010, 31: 809-816.CrossrefGoogle Scholar

  • [3] X. Li, H. Xia, Z. Zhuo, L. Thong, 3D Texture Analysis of Hippocampus Based on MR Images in Patients with Alzheimer Disease, and Mild Cognitive Impairment,” in International Conference on Biomedical Engineering and Informatics, Beijing, 2010.Google Scholar

  • [4] J. Zhang J, Y. Chunsui, and Gui Lian J, 3D texture analysis on MRI images of Alzheimer’s disease, Brain Imaging and Behavior, vol. 6, pp. 61-69, 2012.CrossrefGoogle Scholar

  • [5] Rajeesh J, S.M. Rama, Palinikumar S, Gopalakhrisnan T: Discrimination of Alzheimer’s disease using hippocampus texture features from MRI. Journal Asian Biomedicine 2012, 6: 87-94.Google Scholar

  • [6] Xia H, Tong L, Zhou X, Zhang J: Texture Analysis and Volumetry of Hippocampus and Medial Temporal Lobe in Patients with Alzheimer’s Disease. in International Conference on Biomedical Engineering 2012, Macau, Macao.Google Scholar

  • [7] Simões R, Slump C, Marie A: Using local texture maps of brain MR images to detect Mild Cognitive Impairment. 21st International Conference on Pattern Recognition 2012, JapanGoogle Scholar

  • [8] P. Morgado, M. Silveira, and J.S. Marques, J. Computer Methods in Biomechanics and Biomedical Engineering: 1, 183 (2013)Google Scholar

  • [9] Ojala T: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Journal IEEE Transaction on Pattern Analysis and Machine Intelligence 2002, 24: 971-987.CrossrefGoogle Scholar

  • [10] Pietikainen M, Zhao G, Hadid A, Ahonen T: Local Binary Patterns for Still Images. Computer Vision Using Local Binary Patterns. London: Springer; 2011 13-37.Google Scholar

  • [11] Guo Z, Liu Z, D Zhang: A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing 2010, 19: 1657-1663.Google Scholar

  • [12] Unay D, Ekin A, Cetin M, Jasinchi R, Erchil A: Robustness of Local Binary Patterns in Brain MRI Analysis. in Proc. 29th Ann. Conference of the IEEE EMBS 2007, Lyon.Google Scholar

  • [13] D. Sarwinda and A. Bustamam, Detection of Alzheimer’s disease using advanced local binary pattern from hippocampus and whole brain of MR images, 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 2016, pp. 5051-5056Google Scholar

  • [14] T. Ojala, Multiresolution gray-scale, and rotation invariant texture classification with local binary patterns, Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.CrossrefGoogle Scholar

  • [15] A. C. Rencher, Editor, Methods of Multivariate Analysis, 2nd ed, John Willey & Sons Publishers, Canada, 2002.Google Scholar

  • [16] T. Ahonen, J. Matas, C. He, and M. Pietikainen, Editors. Proceedings of the 16th Annual Scandinavian Conference on Image Analysis, (2009) June 15-18; Oslo, Norway.Google Scholar

  • [17] Nanni L, Lumini A, Brahnam S: Local Binary Pattern Variants as Texture Descriptors for Medical Image Analysis. Artificial Intelligence in Medicine 2010, 49: 117-125.Google Scholar

  • [18] Association A: 2012 Alzheimer’s disease facts and figures. Alzheimer’s and Dementia: The Journal of the Alzheimer’s Association2012, 8:131-168.Google Scholar

  • [19] Ojala T, Pietikinen M, and Menp T: A comparative study of texture measures with classification based on featured distributions. Journal Pattern Recognition 1996, 29: 51-59.Google Scholar

  • [20] Ahonen T, Matas J, He C, Pietikainen M: Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. Proc. 16th Annual Scandinavian Conference on Image Analysis 2009, Norway.Google Scholar

  • [21] M. Das, B. Borah. Biclustering of Gene Expression Data Using Two-Phase Method. International Journal of Computer Applications Vol. 103 No. 13. 2014.Google Scholar

  • [22] H. Turner, T. Bailey, W. Krzanowski. Improved Biclustering of Microarray Data Demonstrated through Systematic Performance Tests. Elseiver. Computational Statistics & Data Analysis, pp. 235 – 254. 2005.CrossrefGoogle Scholar

  • [23] T. Kanungo, D. Mount, N. Netanyahu, et al. An Efficient K-Means Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 24 (7), pp. 881 – 892. 2002.CrossrefGoogle Scholar

  • [24] A. Bustamam, G. Ardaneswari, D. Lestari, H. Tasman. Performance Evaluation of Fast Smith-Waterman Algorithm for Sequence Database Searches using CUDA GPU-Based Parallel Computing. Journal of Next Generation Information Technology Vol. 5 No. 2, pp. 38 – 46. 2014.Google Scholar

  • [25] K.S. Pollard, M.J. Van de Laan. Statistical Inference for Simultaneous Clustering of Gene Expression Data. Math Biosci, 176, pp. 99 – 121. 2002.Google Scholar

  • [26] S.C. Mdaeira, A.L. Oliveira. Biclustering Algorithms for Biological Data Analysis: A Survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1, pp. 24 – 45. 2004.Google Scholar

  • [27] L. Lazzeroni, A. Owen. Plaid Models for Gene Expression Data. Statistica Sinica 12, pp. 61 – 86. 2002.Google Scholar

  • [28] J.A. Hartingan. Clustering Algorithm. New York: John Willey and Sons, Inc. 1997.Google Scholar

  • [29] Zhang D, Wang Y, Zhuo L, Yuan H, Shen D: Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment. Journal Neuroimage 2011, 5: 856-867.CrossrefGoogle Scholar

About the article

Received: 2017-02-21

Accepted: 2017-03-27

Published Online: 2017-11-01

Published in Print: 2018-04-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 8, Issue 2, Pages 111–120, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2018-0008.

Export Citation

© 2018 Alhadi Bustamam et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

Comments (0)

Please log in or register to comment.
Log in