Ultra-short-echo-time MRI may be used to generate imaging biomarkers to phenotype pulmonary abnormalities and facilitate the development of novel treatments but requires clinically-acceptable lung segmentation. We proposed an adaptive kernel K-means approach combining MRI signal intensity and neighbourhood location information for optimized lung segmentation. The resultant high dimensional features were implemented using a K-nearest neighbour graph and relaxed to a point-wise upper-bound formulation regularized by image edge information, which was implemented iteratively using a continuous max-flow optimization approach. Experimental results for 10 asthmatics demonstrated highly accurate, reproducible and computationally efficient lung segmentation for our approach consistent with clinical workflows.
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