Edge Detection in Ventriculograms Using Support Vector Machine Classifiers and Deformable Models

  • Antonio Bravo
  • Miguel Vera
  • Rubén Medina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

In this paper a left ventricle (LV) contour detection method is described. The method works from an approximate contour defined by anatomical landmarks extracted using Support Vector Machine (SVM) classifiers. The LV contour approximation is used as an initialization step for the deformable model algorithm. An optimization method based on a gradient descend algorithm is used to obtain the optimal contour that provides a minimum energy value. Both classifier and edge detection method performances have been validated. The error is determined as the difference between the shape estimated by the algorithm and the shape traced by an expert.

Keywords

anatomical landmarks left ventricle support vector machines edge detection deformable models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Antonio Bravo
    • 1
  • Miguel Vera
    • 2
  • Rubén Medina
    • 3
  1. 1.Grupo de Bioingeniería, Universidad Nacional Experimental del Táchira, Decanato de Investigación, San Cristóbal 5001Venezuela
  2. 2.Laboratorio de Física, Departamento de Ciencias, Universidad de Los Andes–Táchira, San Cristóbal 5001Venezuela
  3. 3.Grupo de Ingeniería Biomédica (GIBULA), Universidad de Los Andes, Facultad de Ingeniería, Mérida 5101Venezuela

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