A Clustering Based Approach for Automatic Image Segmentation: An Application to Biplane Ventriculograms

  • Antonio Bravo
  • Rubén Medina
  • J. Arelis Díaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


This paper reports on an automatic method for ventricular cavity segmentation in angiographic images. The first step of the method consists in applying a linear regression model that exploits the functional relationship between the original input image and a smoothed version. This intermediate result is used as input to a clustering algorithm, which is based on a region growing technique. The clustering algorithm is a two stage process. In the first stage an initial segmentation is achieved using as input the result of the linear regression and the smoothed version of the input image. The second stage is intended for refining the initial segmentation based on feature vectors including the area, the gray-level average and the centroid of each candidate region. The segmentation method is conceptually simple and provides an accurate contour detection for the left ventricle cavity.


Feature Vector Image Segmentation Input Image Segmentation Method Initial Segmentation 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Antonio Bravo
    • 1
  • Rubén Medina
    • 2
  • J. Arelis Díaz
    • 3
  1. 1.Grupo de BioingenieríaUniversidad Nacional Experimental del Táchira, Decanato de InvestigaciónSan CristóbalVenezuela
  2. 2.Facultad de IngenieríaGrupo de Ingeniería Biomédica (GIBULA), Universidad de Los AndesMéridaVenezuela
  3. 3.Laboratorio de Investigación en Matemática Pura y AplicadaUniversidad Nacional Experimental del Táchira, Decanato de InvestigaciónSan CristóbalVenezuela

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