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

Christof Seiler*, Xavier Pennec, Mauricio Reyes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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 languageEnglish
Title of host publicationIEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
PublisherIEEE
Pages452-455
Number of pages4
ISBN (Print)9781424441259
DOIs
Publication statusPublished - 2010
Externally publishedYes

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