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Viewpoint Recognition in Cardiac CT Images

  • Mehdi MoradiEmail author
  • Noel C. Codella
  • Tanveer Syeda-Mahmood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)

Abstract

Position and orientation information is often lacking in DICOM datasets. This creates a need for human involvement or computationally expensive 3D processing for any analytical tool, such as a software-based cognitive assistant, to determine the viewpoint of an input 2D image. We report a solution for cardiac CT viewpoint recognition to identify the desired images for a specific view and subsequent processing and anatomy recognition. We propose a new set of features to describe the global binary pattern of cardiac CT images characterized by the highly attenuating components of the anatomy in the image. We also use five classic image texture and edge feature sets and devise a classification approach based on SVM classification, class likelihood estimation, and majority voting, to classify 2D cardiac CT images into one of six viewpoint categories that include axial, sagittal, coronal, two chamber, four chamber, and short axis views. We show that our approach results in an accuracy of 99.4 % in correct labeling of the viewpoints.

Keywords

Support Vector Machine Random Forest Local Binary Pattern Cardiac Compute Tomography Local Binary Pattern Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mehdi Moradi
    • 1
    Email author
  • Noel C. Codella
    • 2
  • Tanveer Syeda-Mahmood
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA
  2. 2.IBM Thomas J. Watson Research CenterYorktown HeightsUSA

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