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.
Original language | English |
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Title of host publication | IEEE International Symposium on Biomedical Imaging |
Subtitle of host publication | From Nano to Macro |
Publisher | IEEE |
Pages | 452-455 |
Number of pages | 4 |
ISBN (Print) | 9781424441259 |
DOIs | |
Publication status | Published - 2010 |
Externally published | Yes |