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

Alhadi B., Devvi Sarwinda, Gianinna Ardenaswari

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)111-120
Number of pages10
JournalJournal of Artificial Intelligence and Soft Computing Research
Issue number2
Publication statusPublished - 1 Apr 2018


  • Alzheimer's Disease
  • Bi-Clustering
  • Feature Extraction
  • Local Binary Pattern (LBP)
  • MRI


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