Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means
Name
nihms863410.pdf
Description
Accepted version
Size
1.53 MB
Format
Adobe PDF
Checksum (MD5)
0bc57c1ea496ab6eacc00c1a1da5fbf5
Author(s)
Wachinger, Christian
Brennan, Matthew (Matthew Stewart)
Sharp, Greg C.
Golland, Polina
Date Issued
July 2017
Journal
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wachinger, Christian et al. "Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means." IEEE Transactions on Biomedical Engineering 64, 7 (July 2017): 1492 - 1502 © 2017 IEEE
Version
Author's final manuscript
Abstract
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.METHODS: We extend nonlocal 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 nonlocal 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/tbme.2016.2603119
