title: Automated detection of healthy and diseased aortae from images obtained by contrast-enhanced CT scan.
abstract: OBJECTIVE
  We developed the next stage of our computer assisted diagnosis (CAD) system to aid radiologists in evaluating CT images for aortic disease by removing innocuous images and highlighting signs of aortic disease.
  
  
  METHODS
  Segmented data of patient's contrast-enhanced CT scan was analyzed for aortic dissection and penetrating aortic ulcer (PAU). Aortic dissection was detected by checking for an abnormal shape of the aorta using edge oriented methods. PAU was recognized through abnormally high intensities with interest point operators.
  
  
  RESULTS
  The aortic dissection detection process had a sensitivity of 0.8218 and a specificity of 0.9907. The PAU detection process scored a sensitivity of 0.7587 and a specificity of 0.9700.
  
  
  CONCLUSIONS
  The aortic dissection detection process and the PAU detection process were successful in removing innocuous images, but additional methods are necessary for improving recognition of images with aortic disease.
authors.affiliation: Department of Environment and Information Science, Yokohama National University, Yokohama 240-8501, Japan. michaelgayhart@gmail.com
authors.forename: Michael
authors.initials: M
authors.lastname: Gayhart
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Arisawa
mesh_terms: D000465:Algorithms; D000784:Aortic Dissection / Q000000981:diagnostic imaging; D001014:Aortic Aneurysm / Q000000981:diagnostic imaging; D001018:Aortic Diseases / Q000000981:diagnostic imaging*; D001027:Aortography / Q000706:statistics & numerical data*; D019295:Computational Biology; D003287:Contrast Media; D006801:Humans; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*; D014456:Ulcer / Q000000981:diagnostic imaging
chemical_list: D003287:Contrast Media