<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Department of Mathematics, Faculty of Mathematics and Natural Science

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

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

Publié en ligne: 01 nov. 2017

Pages: 111 - 120

Reçu: 21 févr. 2017

Accepté: 27 mars 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0008

Mots clés
Alzheimer’s Disease, MRI, Feature Extraction, Bi-Clustering, Local Binary Pattern (LBP)

© 2018 Alhadi Bustamam et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Publié en ligne: 01 nov. 2017

Pages: 111 - 120

Reçu: 21 févr. 2017

Accepté: 27 mars 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0008

Mots clésAlzheimer’s Disease, MRI, Feature Extraction, Bi-Clustering, Local Binary Pattern (LBP)

© 2018 Alhadi Bustamam et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Alzheimer’s Disease, MRI, Feature Extraction, Bi-Clustering, Local Binary Pattern (LBP)