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Author Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva edit  doi
isbn  openurl
  Title ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences Type Conference Article
  Year 2009 Publication 12th International Conference on Medical Image and Computer Assisted Intervention Abbreviated Journal  
  Volume 5762 Issue II Pages  
  Keywords  
  Abstract The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.  
  Address London, UK  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-04270-6 Medium  
  Area Expedition Conference MICCAI  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ CPF2009 Serial 1228  
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Author Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva edit  openurl
  Title Conditional Random Fields for image segmentation in Intravascular Ultrasound Type Conference Article
  Year 2010 Publication Medical Image Computing in Catalunya: Graduate Student Workshop Abbreviated Journal  
  Volume Issue Pages 13–14  
  Keywords  
  Abstract We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved.  
  Address Girona  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (up)  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference MICCAT  
  Notes MILAB;HUPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ CPF2010 Serial 1453  
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