• DocumentCode
    1771764
  • Title

    Discriminating normal and abnormal left ventricular shapes in four-chamber view 2D echocardiography

  • Author

    Syeda-Mahmood, Tanveer ; Quan Wang ; McNeillie, Patrick ; Beymer, David ; Compas, Colin

  • Author_Institution
    IBM Almaden Res. Center, San Jose, CA, USA
  • fYear
    2014
  • fDate
    April 29 2014-May 2 2014
  • Firstpage
    401
  • Lastpage
    404
  • Abstract
    In this paper, we address discrimination between normal and abnormal left ventricular shapes by capturing deviations from the normal appearance through a new parametric distorted elliptic shape model. To apply the parametric description, we automatically locate the left ventricular region in 4-chamber views and extract its bounding contours and pose. The parametric description of the elliptic fit with minimum alignment error with the bounding contour then becomes the shape descriptor for the bounding contour. Labeled vectors from normal and damaged left ventricular regions are separated into two classes using a support vector machine. Results are presented on a large database of normal and abnormal left ventricular images showing the effectiveness of the parametric features for normal/abnormal discrimination.
  • Keywords
    echocardiography; image matching; medical image processing; support vector machines; 2D echocardiography; abnormal left ventricular shape; damaged left ventricular region; elliptic shape model; four-chamber view; minimum alignment error; normal left ventricular shape; parametric distorted model; shape descriptor; support vector machine; Active shape model; Echocardiography; Heart; Image segmentation; Shape; Support vector machine classification; 4-chamber views; LV segmentation; echocardiography; parametric models; shape matching;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ISBI.2014.6867893
  • Filename
    6867893