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Efficient Descriptor-Based Segmentation of Parotid Glands with Non-Local Means

Institution:
Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Publication Date:
Jul-2017
Journal:
IEEE Trans Biomed Eng
Volume Number:
64
Issue Number:
7
Pages:
1492–1502
Citation:
IEEE Trans Biomed Eng. 2017 Jul; 64(7): 1492–1502.
PubMed ID:
28113224
PMCID:
PMC5469701
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Brennan M., Sharp G., Golland P. Efficient Descriptor-Based Segmentation of Parotid Glands with Non-Local Means. IEEE Trans Biomed Eng. 2017 Jul; 64(7): 1492–1502. PMID: 28113224. PMCID: PMC5469701.
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OBJECTIVE: We introduce descriptor-based segmentation that extends existing patch-based methods by combining intensities, features and location information. Since it is unclear which image features are best suited for patch selection, we perform a broad empirical study on a multitude of different features. We extend non-local means segmentation by including image features and location information. We search larger windows with an efficient nearest neighbor search based on kd-trees. We compare a large number of image features. RESULTS: The best results were obtained for entropy image features, which have not yet been used for patch-based segmentation.We further show that searching larger image regions with an approximate nearest neighbor search and location information yields a significant improvement over the bounded nearest neighbor search traditionally employed in patch-based segmentation methods. CONCLUSION: Features and location information significantly increase the segmentation accuracy. The best features highlight boundaries in the image. SIGNIFICANCE: Our detailed analysis of several aspects of non-local means based segmentation yields new insights about patch and neighborhood sizes together with the inclusion of location information. The presented approach advances the state-of-the-art in the segmentation of parotid glands for radiation therapy planning.