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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Seiler, C. Pennec, X. Reyes, M. |
| Copyright Year | 2010 |
| Description | Author affiliation: University of Bern, ISTB, Switzerland (Seiler, C.; Reyes, M.) || INRIA Sophia, Asclepios Team, Sophia Antipolis, France (Pennec, X.) |
| Abstract | Currently there is an increase usage of CT-based bone diagnosis because low-radiation and cost-effective 2D imaging modalities do not provide the necessary 3D information for bone diagnosis. The fundamental objective of our work is to build a model connecting 2D X-ray information to 3D CT information through regression. As a first step we propose an univariate non-parametric regression on individual predictor variables to explore the non-linearity of the data. To later combine these univariate models we then replace them with parametric models. We examine two predictors, shaft length and caput collum diaphysis angle on a database of 182 CT images of femurs. We show that for each predictor it is possible to describe 99% of the variance through a simple up to second order parametric model. These findings will allow us to extend to the multivariate case in the future. |
| Starting Page | 452 |
| Ending Page | 455 |
| File Size | 174144 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424441259 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2010.5490313 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-04-14 |
| Publisher Place | Netherlands |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biomedical imaging Bones Computed tomography Parametric statistics Medical diagnostic imaging Optical imaging Joining processes X-ray imaging Shafts Image databases Femur Non-Parametric Regression Parametric Regression Log-Euclidean Framework Diffeomorphic Deformations |
| Content Type | Text |
| Resource Type | Article |
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