2012
DOI: 10.1007/978-3-642-33415-3_13
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Incremental Kernel Ridge Regression for the Prediction of Soft Tissue Deformations

Abstract: This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with Finite Element Model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes,… Show more

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Cited by 14 publications
(14 citation statements)
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“…[8][9][10] Among these, FEM is reported to be the most common, accurate and biomechanically relevant method. 11 Nonetheless, the prediction of facial features following orthognathic surgery is still less than ideal, especially around the nose, lips and chin regions, which are critically important for facial esthetics and in evaluating surgical outcome. Reported absolute errors in the predicted lip were greater than 2 mm.…”
Section: Introductionmentioning
confidence: 99%
“…[8][9][10] Among these, FEM is reported to be the most common, accurate and biomechanically relevant method. 11 Nonetheless, the prediction of facial features following orthognathic surgery is still less than ideal, especially around the nose, lips and chin regions, which are critically important for facial esthetics and in evaluating surgical outcome. Reported absolute errors in the predicted lip were greater than 2 mm.…”
Section: Introductionmentioning
confidence: 99%
“…This paper completes our conference paper [18] by including more details of our methods and additional experiments.…”
Section: Introductionmentioning
confidence: 76%
“…The idea was to replace the inner product in H with the kernel function, which could be calculated much more efficiently. Given x and y in the input space and kernel function k , we have the relation [18]…”
Section: Methodsmentioning
confidence: 99%
“…The resolution of the cross-sectional images for gross anatomy were 3072×2048, 0.17 mm of pixel size, and 0.25 mm of image intervals. Eleven muscles (buccinator, depressor anguli oris, depressor labii, levator anguli oris, levator labii, levator labii alaeque nasi, mentalis, orbicularis oris, zygomaticus major, zygomaticus minor and masseter) 24 were segmented from the color images by 2 CMF surgeons (Z.T. and J.J.X.)…”
Section: Methodsmentioning
confidence: 99%