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Texture and gene expression analysis of the MRI brain in detection of Alzheimer’s disease

Bustamam A., Sarwinda D., Ardenaswari G.

Journal of Artificial Intelligence and Soft Computing Research

2018

Vol. 8, No. 2

111--120

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.

Visualisation of Granular Material Concentration Changes, During Silo Discharging Process, Using ECT Large Scale Sensor

Grudzień K., Chaniecki Z., Matusiak B., Romanowski A., Rybak G., Sankowski D.

Image Processing & Communications

2012

Vol. 17, no. 4

327--338

Measuring changes of bulk materials concentration during gravitational flow, a silo emptying is essential information for the assessment of the behaviour and condition of the material during the emptying of the silo. Parameters obtained during this process are important both in terms of process economics and safety, are the basis for monitoring and diagnostics of the process. Affect the current process, but primarily are the result of their filling the silo, and the process of storing the material. Previous studies, conducted by a team of authors, the laboratory-scale silos and numerical calculations and simulations of its increase, helped build the ECT sensor on a large scale. Results related to the change of scale of the sensor and the actual measurements will be discussed in the article. Proposed by the authors of the paper, the method of visualization, performed in the measuring process, helps to ask about the process and suggests a methodology for dealing with the material stored in the silo.

Research of Image Features for Classification of Wear Debris

Juránek R., Machalik S., Zemčik P.

Machine Graphics and Vision

2011

Vol. 20, No. 4

479-493

The wear debris of engineering equipment (such as combustion engines, gearboxes, etc.) consists of metal particles which can be obtained from lubricants used in the equipment. The analysis of wear particles is very important for early detection and prevention of failures. The analysis is often done using classication of individual wear particles obtained by analytical ferrography. In this paper, we present a study of feature extraction methods for a classication of wear particles based on visual similarity. The main contribution of the paper is the comparison of nine selected feature types in the context of three state-of-the-art learning models. Another contribution is the large public database of particle images which can be used for further experiments. The paper describes the dataset, presents the methods of classication, demonstrates the experimental results, and draws conclusions.