Name
Title | ||
|---|---|---|
Early detection of Alzheimer's disease using histograms in a dissimilarity-based classification framework |
Abstract | ||
|---|---|---|
Classification methods have been proposed to detect early-stage Alzheimer's disease using Magnetic Resonance images. In particular, dissimilarity-based classification has been applied using a deformation-based distance measure. However, such approach is not only computationally expensive but it also considers large-scale alterations in the brain only. In this work, we propose the use of image histogram distance measures, determined both globally and locally, to detect very mild to mild Alzheimer's disease. Using an ensemble of local patches over the entire brain, we obtain an accuracy of 84% (sensitivity 80% and specificity 88%). |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/12.2042670 | Proceedings of SPIE |
Keywords | Field | DocType |
Alzheimer's disease,early detection,MRI,dissimilarity-based classification,histogram,local patches | Computer vision,Histogram,Early detection,Entire brain,Artificial intelligence,Image histogram,Distance measures,Physics | Conference |
Volume | ISSN | Citations |
9035 | 0277-786X | 1 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| anne luchtenberg | 1 | 1 | 0.34 |
| Rita Simões | 2 | 3 | 1.42 |
| annemarie van cappellen van walsum | 3 | 1 | 0.34 |
| Cornelis H. Slump | 4 | 189 | 50.31 |