TEMPLATE-BASED AUTOMATIC SEGMENTATION OF MASSETER USING PRIOR KNOWLEDGE

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1 TEMPLATE-BASED AUTOMATIC SEGMENTATION OF MASSETER USING PRIOR KNOWLEDGE H.P. Ng 1,, S.H. Ong 3, P.S. Goh 4, K.W.C. Foong 1, 5, W.L. Nowinski 1 NUS Graduate School for Integrative Sciences and Engineering, Singapore Biomedical Imaging Lab, Agency for Science Technology and Research, Singapore 3 Department of Electrical and Computer Engineering, National University of Singapore 4 Department of Diagnostic Radiology, National University of Singapore 5 Department of Preventive Dentistry, National University of Singapore Abstract In this paper, we propose a knowledge-based, fully automatic methodology for segmenting the masseter, which is a muscle of mastication, from -D magnetic resonance (MR) images for clinical purposes. To our knowledge, there is currently no methodology which automatically segments the masseter from MR images. Our methodology uses five ground truths, where the masseter has been manually segmented and verified by medical experts, to serve as the reference and provide prior knowledge. The prior knowledge involved is the spatial relationship between the region of interest () of the head and of the masseter. In the segmentation process, anisotropic diffusion is first smoothens the of the latter, and thresholding removes unwanted neighboring regions of the masseter. A template of the masseter is then used to obtain an initial segmentation of the muscle, which serves as the initialization to the gradient vector flow (GVF) snake for refining the initial segmentation. We performed -D segmentation of the masseter on a total of 5 MR images, which belong to the mid-facial region through the mandible from five data sets. Validation was done by comparing the segmentation results obtained by using our proposed methodology against manual segmentations done by medical experts, obtaining an average accuracy of 9%. 1. Introduction The muscles of mastication move the mandible at the temporomandibular joint. The large masseter muscle is the strongest jaw muscle. It acts to raise the jaw and clench the teeth. The pterygoid muscles, when used in various combinations, can elevate, depress, or protract the mandible or slide it from side to side. Hence, the proper functioning of the muscles of mastication plays a crucial role as to whether a person can chew or smile properly. When surgeons perform surgery on patients with facial problems, they will have to remove the muscles from the bone, make adjustments to the bone, and re-attach the muscles back to the bone. Through MR imaging and segmentation of the muscles of mastication, surgeons will have more information on the muscles before and after surgery. Numerous image processing techniques have been developed for segmentation MR images. The traditional active contour model [1] has been constantly improved and used extensively in MR image segmentation, examples of which can be found in [] [3]. However, there is currently no image processing technique for the automatic segmentation of muscles of mastication. The task of segmenting muscles of mastication from MR images is difficult due to the fact that the muscles and their surrounding tissue have similar gray levels, with no distinct boundaries between them at times, and we need medical experts to identify the muscles. In this paper, we propose a methodology to segment the masseter from MR images. As compared to the pterygoids and temporalis, the masseter is more easily observable on a MR image because it is relatively larger. The entire methodology can be broken up into two processes, namely the training process and the segmentation process. In the training process, we first develop a spatial relationship between the masseter region of interest () and head in the MR images from the reference data sets where the masseter have been manually segmented. The system is trained to identify the spatial relationship between the masseter and head so that it can automatically detect the masseter in a test image. In the segmentation process, we seek some motivation from the fact that there is an increasing use /06/$0.00/ 006 IEEE. 08

2 on model-based segmentation on MR images, such as the work in [4], which incorporated prior knowledge and segmented the corpus callosum from MR images with little human intervention. In our methodology, we make use of a template of the masseter, which is obtained via manual contour tracings, to obtain an initial segmentation of the facial muscle. This initial segmentation serves as the initialization to the gradient vector flow (GVF) [5] snake to obtain the final segmentation result. The use of GVF snake is preferred over the conventional active contour model, as it is able to converge to concave boundaries. However, the segmentation result is also dependent on good initialization to a certain extent. Hence, in our methodology, we initialize the GVF snake using the initial segmentation which was obtained via the template. After obtaining the final segmentation results, we evaluated the effectiveness of our proposed methodology by comparing our segmentation results against the manual contour tracings provided by medical experts. We obtained an average accuracy of 9% from 5 segmentation results, with the highest accuracy being 95%. This also goes to imply that our proposed method of initializing the GVF snake is capable of producing good segmentation results. The paper first describes the proposed methodology in Section 3 to 5. The segmentation results and discussion are provided in Section 6. We conclude the paper in Section 7.. Data acquisition The MR data were obtained by a 1.5 Tesla MR unit. The MR imaging protocols that were looked into include fluid attenuated inversion recovery (FLAIR), fast spin echo (FSE), gradient echo (GRE), spoiled gradient recall (SPGR) and fast low angle shot (FLASH). A visual comparison of the MR images acquired using the various protocols indicates that the T1 FLASH sequence best displays the anatomy of the facial muscles. Hence, the data sets used in this work are acquired using T1 FLASH (1mm thickness, 51x51 matrix, 40 mm FOV, TR = 9.93, TE = 4.86). A sample of a -D MR image acquired using this imaging protocol is in Figure 1, with the location of the masseter indicated. 3. Overview of methodology The proposed methodology is a -stage process, as illustrated in Figure. The first process defines a spatial relationship between the masseter and the head in images from the reference data sets, which serves as the prior knowledge and the system is trained to identify the masseter in the test image using this spatial information. The second process is the application of the segmentation algorithm on the images from the study data sets. Acquisition of prior knowledge from reference images Images from reference data sets Manual contour tracing of the masseter for each -D image of head and masseter for each image Derive a relationship between head and masseter for each image Obtain an average of this relationship for all these reference images Figure 1. -D MR image Figure. Overview of methodology 4. Acquisition of prior knowledge S egmentation process on study images Input image from test data sets of head of masseter by the system using prior knowledge Apply anisotropic diffusion to masseter Thresholding to remove fats and bone Correlation with template of masseter for initial segmentation Perform GVF snake to refine initial segmentation We first get medical experts to manually segmenting the facial muscles of interest. Next, we automatically detect the region of interest () of the head, which bounds the entire surface of the head in a -D image as illustrated in Figure 4, through the 09

3 projections of the image in the x-direction (horizontal) and the y-direction (vertical). This is illustrated in Figure 3. After which, the of the masseter is also automatically detected. 4 x x 104 are w and b respectively, we derive the equations for j, k, x, y as illustrated in Figure 6. in study image is scaled by a factor s Start 1.5 point End point w1 j1 distance y1 distance x1 (a) (b) Figure 3. Projections of MR image in (a) vertical, (b) horizontal directions k1 After automatically obtaining the head and masseter, we define a spatial relationship between them. This relationship is in terms of the distance between the boundaries of the head and origin of masseter, as illustrated in Figure 4. To have a good estimate of the spatial relationship, we obtain the arithmetic average of the spatial relationship between the boundaries of the head and the origin of masseter from the reference images. The averages of the various spatial distances, w1, j1, k1, x1, y1, as illustrated in Figure 5 were calculated. Through the reference images where the masseter has been manually segmented, we obtain the template of the masseter. Head Head Figure 5. Spatial relationship between head and masseter in reference image w j = j1 s w 1 w b b k = k1 s w y = y1 w1 b x = x1 Head Figure 6. Spatial relationship between head and origin of masseter in study image Figure 4. Head and masseter 5. Segmentation process Given an image from the test data set, the system first automatically determines the head based on the vertical and horizontal projections, which was described in the previous section. The system then makes use of the spatial relationship which was acquired earlier on to identify the masseter in the input image. Given that the and of the head in the input image The masseter consists of bundles of multinucleated cells, called muscle fibers, as shown in Figure 7. Hence, the masseter has a coarse surface due to the similarity in intensities between the regions in the of the masseter, which will result in inaccurate results when we make use of active contour and gradient vector flow techniques. We make use of anisotropic diffusion [6] (number of iterations=0, time-step = 0., 1 smoothing per iteration) to smoothen it. Figure 7. 10

4 Fat and bone have different gray levels from soft tissue. From Figure 7, it can be observed that the soft tissue occupies the most number of pixels in the masseter. We use a thresholding approach which first divides the histogram into bins with gray level intervals of 30, and we deposit the pixels into their respective bins. The bin with the most number of pixels remains, as it contains the pixels belonging to the soft tissue. An example of the of masseter, after thresholding, is in Figure 8(a). We then shift the template of the masseter, shown in Figure 8(b), across the of the masseter (left to right; top to bottom), and check for their correlation. If the region in the masseter is similar to the prototype by more than 50%, the region will remain, else it will be removed. In addition, we use morphological operator closing to fill up the holes in the masseter. Closing is simply as a dilation followed by an erosion using the same structuring element, which in our case is a 3x3 matrix of 1 s, for both operations. Connected components labeling is then used to remove the small and unwanted regions in the masseter, thus obtaining an initial segmentation of the masseter. Figure 9, we show a set of results obtained after each stage of our method. In Figure 10, we display the segmentation results of some other images. Numerical validations are performed on the 5 segmentation results by comparisons with the manual segmentations. The formula: N ( M S ) accuracy = 100% ( ) ( ) N M + N S is used, where M and S are the s belonging to the manual segmentations and our segmentation results respectively. N ( M ) refers to the number of pixels in M. The proposed methodology is able to achieve an average accuracy of 9% in 5 segmentation results. Original MR image with identified of masseter anisotropic diffusion thresholding correlation with template removing small regions Initialization of GVF snake Final Segmentation (a) (b) Figure 8. (a) thresholding (b) template We obtain the edge map of the initial segmentation by using the canny filter, and use it as initialization for the GVF snake [5] to refine the initial segmentation of the masseter. GVF is based on diffusion of gradient vectors. Mathematically, GVF is defined as the vector field ν ( x, y) = ( u( x, y), v( x, y) ) that minimizes the energy functional. ε = µ ( u + u + v + v ) + f ν f x y x y x where µ is a constant that depends on the amount of noise present in the image and f is the gradient of the edge map. y Figure 9. Results at each stage of methodology 6. Results and discussion We performed -D segmentation of the masseter on a total of 5 MR images, in the mid-facial region through the mandible, from five different data sets. In Figure 10. Segmentation results 11

5 Segmenting muscles from MR images is difficult because the muscle and its surrounding tissue have similar gray levels, with no distinct boundaries between them at times. Despite this, our proposed methodology, which involves prior knowledge on the location of the muscle and a template of the muscle, is able to perform segmentation of the muscle with accuracy of more than 90%. Our methodology is automatic and the results are not subjected to intra- and inter-observer variations. We plan to apply our proposed methodology to segment the pterygoids and the temporalis. Our proposed methodology is used for -D medical image segmentation but we do realize that there is an increasing emphasis on 3-D medical image segmentation and many established techniques such as the level set approach [7] and the watersnakes [8] are applied on 3-D medical image segmentation. Hence, as part of our future work, we will also look forward to extending our methodology for 3-D medical image segmentation. 7. Conclusions In this paper, we propose a fully automatic method for segmenting the masseter, one of the muscles of mastication, from -D MR images. To our best knowledge, this is currently not available. Our template-guided method involves prior knowledge on the location of masseter with respect to the head, obtained from ground truths by medical experts. These manual contour tracings also provide us with the template used in obtaining the initial segmentations. We make use of the GVF snake to refine the initial segmentations of the facial muscle, using the initial segmentations as initializations to the GVF snake. The proposed methodology is able to achieve an average accuracy of 9% in 5 segmentation results. Its advantages in using our proposed method include it is fully automatic and hence the segmentation results will not be subjected to intra- and inter-observer variations. In addition, our proposed method is independent of the location of the masseter inside its. The GVF snake is dependent on the initialization, to a certain extent, to produce good segmentations. Our proposed methodology uses the initial segmentation obtained via template matching to serve as the initialization to the GVF snake. These initial segmentations are near to the actual boundaries, and hence the computation time to arrive at the final segmentation from the initialization is short (< 6sec). 8. Acknowledgements The first author will like to thank Agency for Science Technology and Research (A*Star), Singapore for funding his PhD study. The author will also like to thank Mr Christopher Au, Principal Radiographer at National University Hospital, Singapore for rendering his help and expertise in data acquisition. 9. References [1] M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models, Int. Journal of Computer Vision, Vol.1, 1987, pp [] N. Ray, S.T. Acton, T. Altes, E.E. De Lange, J.R. Brookeman, Merging Parametric Active Contours Within Homogeneous Image Regions for MRI-based Lung Segmentation, IEEE Trans. on Medical Imaging, Vol., Issue, 003, pp [3] C. Pluempitiwiriyawej, J.M.F. Moura, Y.J. Lin Wu, C. Ho, STACS: New Active Contour Scheme for Cardiac MR Image Segmentation, IEEE Trans. on Medical Imaging, Vol. 4, Issue 5, 005, pp [4] A. Lundervold, N. Duta, T. Taxt, A.K. Jain, Modelguided Segmentation of Corpus Callosum in MR Images, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.1, 1999, pp [5] C. Xu, J.L. Prince, Snakes, Shapes, and Gradient Vector Flow, IEEE Trans. on Image Processing, Vol.7, 1998, pp [6] P. Perona, J. Malik, Scale-Space and Edge Detection using Anisotropic Diffusion, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.1, 1990, pp [7] R. Malladi, J.A. Sethian, B.C. Vermuri, Shape modelling with front propagation: A level set approach, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 17(), 1995, pp [8] H.T. Nguyen, M. Worring, R.V.D. Boomgaard, Watersnakes: Energy-driven watershed segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.5, 003, pp