Quantitative analysis of human masticatory muscles using magnetic resonance imaging.

OBJECTIVES: The objective of this study was to quantitatively evaluate the correlation between left and right masticatory muscle volumes in normal subjects. METHODS: Contiguous 1 mm MR scans were obtained of 12 normal adult subjects aged 20-25 years using a Siemens 1.5 T MR scanner. The volumes of the human masticatory muscles (masseter, lateral and medial pterygoid) were measured from the scans using our previously proposed method. To test for inter- and intraobserver reproducibility, measurements were performed by two users on two separate occasions, with a span of 2 weeks between them and with the previous results blinded. Good inter- and intraobserver reproducibility was achieved in our study. RESULTS: The mean volumes for left and right masseters, and lateral and medial pterygoids were 29.54 cm3, 29.65 cm3, 9.47 cm3, 10.23 cm3, 8.69 cm3 and 8.92 cm3, respectively. The Pearson correlation coefficients between the volumes of the left and right masseters, lateral and medial pterygoids are 0.969, 0.906 and 0.924, respectively. CONCLUSIONS: The computed volumes of the masticatory muscles show a strong correlation between the volumes of the left and right masseters, and lateral and medial pterygoids for normal adult subjects. The total masticatory muscle volume on the left and right sides of normal subjects is similar.

3D segmentation and quantification of a masticatory muscle from MR data using patient-specific models and matching distributions.

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 27.72 cm(3)) are higher than those of older (ethnic group removed) adults (23.16 and 22.13 cm(3)). 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.

Masseter segmentation using an improved watershed algorithm with unsupervised classification.

The watershed algorithm always produces a complete division of the image. However, it is susceptible to over-segmentation and sensitivity to false edges. In medical images this leads to unfavorable representations of the anatomy. We address these drawbacks by introducing automated thresholding and post-segmentation merging. The automated thresholding step is based on the histogram of the gradient magnitude map while post-segmentation merging is based on a criterion which measures the similarity in intensity values between two neighboring partitions. Our improved watershed algorithm is able to merge more than 90% of the initial partitions, which indicates that a large amount of over-segmentation has been reduced. To further improve the segmentation results, we make use of K-means clustering to provide an initial coarse segmentation of the highly textured image before the improved watershed algorithm is applied to it. When applied to the segmentation of the masseter from 60 magnetic resonance images of 10 subjects, the proposed algorithm achieved an overlap index (kappa) of 90.6%, and was able to merge 98% of the initial partitions on average. The segmentation results are comparable to those obtained using the gradient vector flow snake.

Knowledge-driven 3-D extraction of the masseter from MR data.

In this paper, we propose a knowledge-driven highly automatic methodology for extracting the masseter from magnetic resonance (MR) data sets for clinical purposes. The masseter is a muscle of mastication which acts to raise the jaw and clench the teeth. In our initial work, we designed a process which allowed us to perform 2-D segmentation of the masseter on 2-D MR images. In the methodology proposed here, we make use of ground truth to first determine the index of the MR slice in which we will carry out 2-D segmentation of the masseter. Having obtained the 2-D segmentation, we will make use of it to determine the region of interest (ROI) of the masseter in the other MR slices belonging to the same data set. The upper and lower thresholds applied to these MR slices, for extraction of the masseter, are determined through the histogram of the 2-D segmented masseter. Visualization of the 3-D masseter is achieved via volume rendering. Our methodology has been applied to five MR data sets. Validation was done by comparing the segmentation results obtained by using our proposed methodology against manual contour tracings, obtaining an average accuracy of 83.5%