Name
Abstract | ||
|---|---|---|
Abstract: Elastography is a new imaging modality, which provides an image of tissue elastic properties. Simulated elastograms are used to study the ability of textural features to discriminate abnormalities with different levels of hardness and density. Elastograms are simulated for different signal processing parameters as well as non-uniform stress field. Fifteen selected texture features are extracted. The Gaussian classifier does not perform well due to complexity of clusters. Hence, a neural network classifier is trained and tested to discriminate the classes. The results indicate the texture features perform well under variations of the signal processing parameters, and non-uniformity of the applied stress field. This simulation study provides a guiding mechanism to discriminate between malignant and benign tissue abnormalities once clinical data becomes available. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/CBMS.2001.941762 | CBMS |
Keywords | Field | DocType |
cancer,stress,hardness,biomechanics,neural nets,simulation,feature extraction,signal processing,elasticity,density,neural networks,medical simulation,capacitive sensors,image texture,testing,image classification,radio frequency | Stress field,Signal processing,Computer vision,Pattern recognition,Computer science,Image texture,Feature extraction,Artificial intelligence,Gaussian classifier,Artificial neural network,Contextual image classification,Elastography | Conference |
ISSN | ISBN | Citations |
1063-7125 | 0-7695-1004-3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nasser D. Kehtarnavaz | 1 | 198 | 20.74 |
| Babak Nadjar Araabi | 2 | 383 | 44.18 |
| Kallel, F. | 3 | 0 | 0.34 |
| Ophir, J. | 4 | 0 | 0.34 |