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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Reuze, P. Bruno, A. Le Rumeur, E. |
| Copyright Year | 1994 |
| Description | Author affiliation: Lab. Traitement du Signal et de l'Image, Rennes I Univ., France (Reuze, P.; Bruno, A.) |
| Abstract | Textural features are compared for the classification of MR muscle images. The objective is to determine which features optimize classification rate using small ROIs. Four classes of textural features are considered: the authors have studied fractal, cooccurrence, higher order statistics and mathematical morphology. The quantitative evaluation of the discrimination power of the features is based on the performance of the classification error rate with a K-nearest neighbor classifier. The results shows that the mathematical morphology features provide the best classification rate on the authors' clinical MR images of healthy and sick muscles.< |
| Starting Page | 645 |
| Ending Page | 646 |
| File Size | 219710 |
| Page Count | 2 |
| File Format | |
| ISBN | 0780320506 |
| DOI | 10.1109/IEMBS.1994.411843 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-11-03 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Muscles Morphology Fractals Higher order statistics Error analysis |
| Content Type | Text |
| Resource Type | Article |
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