Abstract
A novel task-based algorithm performance evaluation technique is propose for the evaluation of geometry-driven diffusion (GDD) methods used for increasing the signal-to-noise ratio in MR tomograms. It is based on a probabilistic model of stepwise constant image corrupted the distribution parameters of the ran-dom variable derived from intensity gradient are used for characterization of staircase image artifacts in diffused images. The proposed evaluation tech-nique incorporates a "gold standard" of the GDD algorithms, defined as a dif-fusion process governed by ideal values of conductance
Originalsprache | Englisch |
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Titel | erschienen in: Empirical Evaluation Methods in Computer Vision. Series in machine perception and artificial intelligence. Ed. by: Christensen Henrik I. |
Seiten | 89-115 |
Seitenumfang | 27 |
Publikationsstatus | Veröffentlicht - 2002 |
Research Field
- Nicht definiert