• DocumentCode
    617622
  • Title

    Automatic view classification of echocardiograms using Histogram of Oriented Gradients

  • Author

    Agarwal, Deborah ; Shriram, K.S. ; Subramanian, Nachiappan

  • Author_Institution
    IIT Delhi, New Delhi, India
  • fYear
    2013
  • fDate
    7-11 April 2013
  • Firstpage
    1368
  • Lastpage
    1371
  • Abstract
    When imaging the heart, using a 2D ultrasound probe, different views can manifest depending on the location and angulations of the probe. Some of these views have been labeled as standard views, due to the presentation and ease of assessment of key cardiac structures in them. We present an approach for automatic recognition and classification of these standard views - namely the Parasternal Long Axis (PLAX) and the Short Axis (SAX) B-mode echocardiograms. The Histogram of Oriented Gradients (HOG) used as the discriminating feature encodes the spatial arrangement of edges/gradients in the images. The HOG feature is computed on the pre-scan converted image data in the ultrasound beam space. On a fairly large database of 703 images, with a Support Vector Machine classifier we obtained an accuracy of about 98%.
  • Keywords
    echocardiography; image classification; medical image processing; support vector machines; 2D ultrasound probe; HOG feature; automatic recognition; automatic view classification; echocardiograms; heart imaging; histogram; parasternal long axis B-mode echocardiograms; prescan converted image data; short axis B-mode echocardiograms; support vector machine classifier; ultrasound beam space; Conferences; Histograms; Imaging; Kernel; Support vector machines; Ultrasonic imaging; Vectors; Echocardiography; HOG; View classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on
  • Conference_Location
    San Francisco, CA
  • ISSN
    1945-7928
  • Print_ISBN
    978-1-4673-6456-0
  • Type

    conf

  • DOI
    10.1109/ISBI.2013.6556787
  • Filename
    6556787