Abstract
A method is proposed for 3D segmentation and quantification of the masseter muscle from magnetic resonance (MR) images, which is often performed in pre-surgical planning and diagnosis. Because of a lack of suitable automatic techniques, a common practice is for clinicians to manually trace out all relevant regions from the image slices which is extremely time-consuming. The proposed method allows significant time savings. In the proposed method, a patient-specific masseter model is built from a test dataset after determining the dominant slices that represent the salient features of the 3D muscle shape from training datasets. Segmentation is carried out only on these slices in the test dataset, with shape-based interpolation then applied to build the patient-specific model, which serves as a coarse segmentation of the masseter. This is first refined by matching the intensity distribution within the masseter volume against the distribution estimated from the segmentations in the dominant slices, and further refined through boundary analysis where the homogeneity of the intensities of the boundary pixels is analyzed and outliers removed. It was observed that the left and right masseter muscles’ volumes in young adults (28.54 and 27.72cm3) are higher than those of older (ethnic group removed) adults (23.16 and 22.13cm3). Evaluation indicates good agreement between the segmentations and manual tracings, with average overlap indexes for the left and right masseters at 86.6% and 87.5% respectively.
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Acknowledgements
The first author will like to thank Agency for Science, Technology and Research (A*STAR), Singapore for funding his PhD studies. The authors thank Mr Christopher Au, Principal Radiographer at National University Hospital, Singapore for his assistance in data acquisition which is funded by NUS R-222-000-023-112 from the Faculty of Dentistry, National University of Singapore.
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Ng, H.P., Ong, S.H., Liu, J. et al. 3D Segmentation and Quantification of a Masticatory Muscle from MR Data Using Patient-Specific Models and Matching Distributions. J Digit Imaging 22, 449–462 (2009). https://doi.org/10.1007/s10278-008-9132-1
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DOI: https://doi.org/10.1007/s10278-008-9132-1