Parametric regression of 3D medical images through the exploration of non-parametric regression models

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.
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Seventh IEEE International Symposium on Biomedical Imaging 2010 (ISBI'10), 2010, Rotterdam, Netherlands. IEEE, pp.452--455, 2010, Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. 〈10.1109/ISBI.2010.5490313〉
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Christof Seiler, Xavier Pennec, Mauricio Reyes Aguirre. Parametric regression of 3D medical images through the exploration of non-parametric regression models. Seventh IEEE International Symposium on Biomedical Imaging 2010 (ISBI'10), 2010, Rotterdam, Netherlands. IEEE, pp.452--455, 2010, Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on. 〈10.1109/ISBI.2010.5490313〉. 〈inria-00616168〉

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