Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11548-006-0046-4
Title: Muscles of mastication model-based MR image segmentation
Authors: Ng, H.P.
Ong, S.H. 
Hu, Q.
Foong, K.W.C. 
Goh, P.S.
Nowinski, W.L.
Keywords: Masseter
Model-based segmentation
MRI
Muscles
Pterygoids
Issue Date: Nov-2006
Source: Ng, H.P., Ong, S.H., Hu, Q., Foong, K.W.C., Goh, P.S., Nowinski, W.L. (2006-11). Muscles of mastication model-based MR image segmentation. International Journal of Computer Assisted Radiology and Surgery 1 (3) : 137-148. ScholarBank@NUS Repository. https://doi.org/10.1007/s11548-006-0046-4
Abstract: Objective: The muscles of mastication play a major role in the orodigestive system as the principal motive force for the mandible. An algorithm for segmenting these muscles from magnetic resonance (MR) images was developed and tested. Materials and methods: Anatomical information about the muscles of mastication in MR images is used to obtain the spatial relationships relating the muscle region of interest (ROI) and head ROI. A model-based technique that involves the spatial relationships between head and muscle ROIs as well as muscle templates is developed. In the segmentation stage, the muscle ROI is derived from the model. Within the muscle ROI, anisotropic diffusion is applied to smooth the texture, followed by thresholding to exclude bone and fat. The muscle template and morphological operators are employed to obtain an initial estimate of the muscle boundary, which then serves as the input contour to the gradient vector flow snake that iterates to the final segmentation. Results: The method was applied to segmentation of the masseter, lateral pterygoid and medial pterygoid in 75 images. The overlap indices (κ) achieved are 91.4, 92.1 and 91.2%, respectively. Conclusion: A model-based method for segmenting the muscles of mastication from MR images was developed and tested. The results show good agreement between manual and automatic segmentations. © 2006 CARS.
Source Title: International Journal of Computer Assisted Radiology and Surgery
URI: http://scholarbank.nus.edu.sg/handle/10635/56747
ISSN: 18616410
DOI: 10.1007/s11548-006-0046-4
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