Masseter segmentation using an improved watershed algorithm with unsupervised classification

Authors:
H. P. Ng;S. H. Ong;K. W. C. Foong;P. S. Goh;W. L. Nowinski
Affiliations:
NUS Graduate School for Integrative Sciences and Engineering, Singapore and Biomedical Imaging Lab, Agency for Science Technology and Research, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore and Division of Bioengineering, National University of Singapore, Singapore;NUS Graduate School for Integrative Sciences and Engineering, Singapore and Department of Preventive Dentistry, National University of Singapore, Singapore;Department of Diagnostic Radiology, National University of Singapore, Singapore;Biomedical Imaging Lab, Agency for Science Technology and Research, Singapore
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

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 (@k) 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.