Name
Title | ||
|---|---|---|
Alternative feature extraction methods in 3D brain image-based diagnosis of Alzheimer's Disease. |
Abstract | ||
|---|---|---|
Positron Emission Tomography plays an important role as an Alzheimer's Disease (AD) early diagnosis tool, and also identifying Mild Cognitive Impairment (MCI) patients. The vast majority of 3D brain image-based computer aided diagnosis methods implemented so far relied simply on voxel intensity, as feature. In this article, we consider two alternative methods of feature extraction: 3D Haarlike features and histograms of gradient magnitude and orientation; their performance in the classification of AD vs. Cognitively Normal (CN), MCI vs. CN and AD vs. MCI patients is evaluated and compared to the one obtained when using voxel intensity only. Classification is accomplished through Support Vector Machines, after an automatic feature selection step. The features based on histograms of the gradient attained the best results in AD vs. CN discrimination, and 3D Haar-like features improved performance in all three classification tasks. These improvements encourage further investigation on these extraction strategies. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6467090 | ICIP |
Keywords | Field | DocType |
brain,cognition,diseases,feature extraction,image classification,image resolution,medical image processing,positron emission tomography,support vector machines,3D Haar-like features,3D brain image-based computer aided diagnosis methods,AD vs. CN discrimination,AD vs. MCI patient classification,AD vs. cognitively normal patient classification,Alzheimer's disease early diagnosis tool,MCI,MCI vs. CN patient classification,automatic feature selection step,feature extraction methods,histograms-of-gradient magnitude-and-orientation,mild cognitive impairment patient identification,positron emission tomography,support vector machines,voxel intensity,Alzheimer's disease,Computer aided diagnosis,Feature Extraction,Positron Emission Tomography | Voxel,Computer vision,Histogram,Pattern recognition,Feature selection,Computer science,Computer-aided diagnosis,Support vector machine,Feature extraction,Artificial intelligence,Positron emission tomography,Contextual image classification | Conference |
ISSN | Citations | PageRank |
1522-4880 | 3 | 0.41 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eduardo Bicacro | 1 | 6 | 0.86 |
| Margarida Silveira | 2 | 109 | 10.48 |
| Jorge S. Marques | 3 | 535 | 67.78 |