Three-dimensional spine model reconstruction using one-class SVM regularization

  1. Get@NRC: Three-dimensional spine model reconstruction using one-class SVM regularization (Opens in a new window)
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Journal titleIEEE Transactions on Biomedical Engineering
Article number6557062
Pages32563264; # of pages: 9
Subject3D reconstruction; Biomedical applications; One-class support vector machines (OCSVM); scoliosis; State-of-the-art methods; Statistical shape model; Gaussian distribution; Hospital data processing; Medical applications; Support vector machines; accuracy; algorithm; anatomic model; image reconstruction; sensitivity analysis; statistical shape model; validity
AbstractStatistical shape models have become essential for medical image registration or segmentation and are used in many biomedical applications. These models are often based on Gaussian distributions learned from a training set. We propose in this paper a shape model which does not rely on the estimation of a Gaussian distribution, but on similarities computed with a kernel function. Our model takes advantage of the one-class support vector machine (OCSVM) to do so. In this context, we propose in this paper a method for reconstructing the spine of scoliotic patients using OCSVM regularization. Current state-of-the-art methods use conventional statistical shape models, and the reconstruction is commonly processed by minimizing a Mahalanobis distance. Nevertheless, when a shape differs significantly from the statistical model, the associated Mahalanobis distance often overstates the need for statistical regularization. We show that OCSVM regularization is more robust and is less sensitive to weak landmarks definition and is hardly influenced by the presence of outliers in the training data. The proposed OCSVM model applied to 3-D spine reconstruction was evaluated on real patient data, and results showed that our approach allows precise reconstruction.
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AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21270442
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Record identifiere3ad19b5-e670-4b90-b58c-817120035848
Record created2014-02-11
Record modified2016-05-09
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