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Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling
Date
2016-02-01
Author
Şener, Emre
Mumcuoğlu, Ünal Erkan
Hamcan, Salih
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Background: Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification.
Subject Keywords
Image segmentation
,
Information fusion
,
Partial volume
,
Resolution enhancement
,
Superresolution
URI
https://hdl.handle.net/11511/30461
Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
DOI
https://doi.org/10.1016/j.cmpb.2015.10.009
Collections
Graduate School of Informatics, Article
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Segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification. In this study, a new Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin. The method incorporates a model to account for image blurring during data acquisition, a prior helping to reduce noise as well as a partial volume model. Regularization ba...
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BibTeX
E. Şener, Ü. E. Mumcuoğlu, and S. Hamcan, “Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling,”
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
, pp. 31–44, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30461.