2016
DOI: 10.1371/journal.pone.0152381
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The Accuracy of Conformation of a Generic Surface Mesh for the Analysis of Facial Soft Tissue Changes

Abstract: PurposeThree dimensional analysis of the face is required for the assessment of complex changes following surgery, pathological conditions and to monitor facial growth. The most suitable method may be “dense surface correspondence”.Materials and MethodsThis method utilizes a generic facial mesh and “conformation process” to establish anatomical correspondences between two facial images. The aim of this study was to validate the use of conformed meshes to measure simulated maxillary and mandibular surgical move… Show more

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Cited by 16 publications
(9 citation statements)
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“…Using the "shape transfer function" in DiView the generic mesh was conformed to the 3D facial image to produce a "conformed mesh", this procedure is described briefly below and in greater detail elsewhere. 15 All the conformed UCLP meshes and conformed reference images were saved in .OBJ format. Using MorphAnaylser software (http://cherry.dcs.aber.ac.uk/morphanalyser) an individualised average facial was produced for the UCLP group and the reference group.…”
Section: Average Facial Template Constructionmentioning
confidence: 99%
“…Using the "shape transfer function" in DiView the generic mesh was conformed to the 3D facial image to produce a "conformed mesh", this procedure is described briefly below and in greater detail elsewhere. 15 All the conformed UCLP meshes and conformed reference images were saved in .OBJ format. Using MorphAnaylser software (http://cherry.dcs.aber.ac.uk/morphanalyser) an individualised average facial was produced for the UCLP group and the reference group.…”
Section: Average Facial Template Constructionmentioning
confidence: 99%
“…The mesh is a mathematical facial mask representing the children’s faces by a fixed number of indexed vertices. The conformation of the generic facial mesh on the 3-D facial morphology is a mathematical “wrapping” of the mesh on the morphology of the 3-D image (Cheung et al, 2016). This allows the most accurate registration/superimposition of the anatomical corresponding points of any 2 images (Claes et al, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…As a consequence of this technique and the nature of the alignment algorithm, the meshes may have been considered to be wellaligned (based on a low distance deviation) despite the true anatomical points potentially being offset. 15 This may have led to slight overestimations in our values for accuracy.…”
Section: Discussionmentioning
confidence: 91%