Comput Methods Programs Biomed - Automatic segmentation of human facial tissue by MRI-CT fusion: a feasibility study.

Tópicos

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Resumo

The aim of this study was to develop automatic image segmentation methods to segment human facial tissue which contains very thin anatomic structures. The segmentation output can be used to construct a more realistic human face model for a variety of purposes like surgery planning, patient specific prosthesis design and facial expression simulation. Segmentation methods developed were based on Bayesian and Level Set frameworks, which were applied on three image types: magnetic resonance imaging (MRI), computerized tomography (CT) and fusion, in which case information from both modalities were utilized maximally for every tissue type. The results on human data indicated that fusion, thickness adaptive and postprocessing options provided the best muscle/fat segmentation scores in both Level Set and Bayesian methods. When the best Level Set and Bayesian methods were compared, scores of the latter were better. Number of algorithm parameters (to be trained) and computer run time measured were also in favour of the Bayesian method.

Resumo Limpo

aim studi develop automat imag segment method segment human facial tissu contain thin anatom structur segment output can use construct realist human face model varieti purpos like surgeri plan patient specif prosthesi design facial express simul segment method develop base bayesian level set framework appli three imag type magnet reson imag mri computer tomographi ct fusion case inform modal util maxim everi tissu type result human data indic fusion thick adapt postprocess option provid best musclefat segment score level set bayesian method best level set bayesian method compar score latter better number algorithm paramet train comput run time measur also favour bayesian method

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