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Supervised Learning with Global Features for Image Retrieval in Atlas-Based Segmentation of Thoracic CT

Hua Ma, Thomas Coradi, Gabor Székely, Benjamin Haas, Orcun Goksel
Int Congress on Computer Assisted Radiology and Surgery (CARS)
Heidelberg, Germany, June 2013

Abstract

Atlas-based segmentation is an essential component of computer aided planning for radiotherapy. Commercial products often have access to a large number of candidate images to be used as atlases and thus efficient mechanisms are necessitated to automatically retrieve suitable atlas images. In this study, we have first developed methods to extract global features from thoracic CT images. These include geometrical features based on both voxel intensities and the outlines of automatic approximate bone, lung, and whole-body segmentations that can be calculated in seconds. Our goal is to study image retrieval techniques using these global image features, in particular investigating the feasibility of various supervised learning algorithms. Such retrieved images are then to be used as atlasses for the atlas-based segmentation of anatomy that cannot be segmented automatically such as lymph nodes.


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@InProceedings{eth_biwi_01043,
  author = {Hua Ma and Thomas Coradi and Gabor Székely and Benjamin Haas and Orcun Goksel},
  title = {Supervised Learning with Global Features for Image Retrieval in Atlas-Based Segmentation of Thoracic CT},
  booktitle = {Int Congress on Computer Assisted Radiology and Surgery (CARS)},
  year = {2013},
  month = {June},
  pages = {S302},
  keywords = {}
}