A Statistical Model for Point-Based Target Registration Error With Anisotropic Fiducial Localizer Error
| Autor: | Don D. Frantz, Andrew D. Wiles, A. Likholyot, Terry M. Peters |
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| Rok vydání: | 2008 |
| Předmět: |
Computer science
Image registration Context (language use) Models Biological Sensitivity and Specificity Pattern Recognition Automated Normal distribution Artificial Intelligence Image Interpretation Computer-Assisted Medical imaging Computer Simulation Computer vision Electrical and Electronic Engineering Models Statistical Radiological and Ultrasound Technology Covariance matrix business.industry Reproducibility of Results Statistical model Image Enhancement Computer Science Applications Data Interpretation Statistical Subtraction Technique Anisotropy Artificial intelligence Artifacts Fiducial marker business Algorithms Software |
| Zdroj: | IEEE Transactions on Medical Imaging. 27:378-390 |
| ISSN: | 0278-0062 |
| DOI: | 10.1109/tmi.2007.908124 |
| Popis: | Error models associated with point-based medical image registration problems were first introduced in the late 1990s. The concepts of fiducial localizer error, fiducial registration error, and target registration error are commonly used in the literature. The model for estimating the target registration error at a position r in a coordinate frame defined by a set of fiducial markers rigidly fixed relative to one another is ubiquitous in the medical imaging literature. The model has also been extended to simulate the target registration error at the point of interest in optically tracked tools. However, the model is limited to describing the error in situations where the fiducial localizer error is assumed to have an isotropic normal distribution in R3. In this work, the model is generalized to include a fiducial localizer error that has an anisotropic normal distribution. Similar to the previous models, the root mean square statistic rmstre is provided along with an extension that provides the covariance matrix Sigmatre. The new model is verified using a Monte Carlo simulation and a set of statistical hypothesis tests. Finally, the differences between the two assumptions, isotropic and anisotropic, are discussed within the context of their use in 1) optical tool tracking simulation and 2) image registration. |
| Databáze: | OpenAIRE |
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