title: Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means.
abstract: 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 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.
  
  
  
  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.
  
  
  
  Features and location information significantly increase the segmentation accuracy. The best features highlight boundaries in the image.
  
  
  
  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.
authors.forename: Christian
authors.initials: C
authors.lastname: Wachinger
authors.forename: Matthew
authors.initials: M
authors.lastname: Brennan
authors.forename: Greg C
authors.initials: GC
authors.lastname: Sharp
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D010306:Parotid Gland / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*