Segmentation of 3D cardiac ultrasound images using correlation of radio frequency data

  • Authors:

  • M. M. Nillesen;R. G. P. Lopata;I. H. Gerrits;H. J. Huisman;J. M. Thijssen;L. Kapusta;C. L. de Korte

  • Affiliations:

  • Clinical Physics Laboratory, Department of Pediatrics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Clinical Physics Laboratory, Department of Pediatrics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Clinical Physics Laboratory, Department of Pediatrics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Department of Radiology, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Clinical Physics Laboratory, Department of Pediatrics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Pediatric Cardiology, Department of Pediatrics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Clinical Physics Laboratory, Department of Pediatrics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands

  • Venue:
  • ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
  • Year:
  • 2009

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Abstract

Semi-automatic segmentation of the heart muscle in 3D echographic images may substantially support clinical diagnosis of heart disease. Especially in children with congenital heart disease, segmentation should be based on the echo features solely since a priori knowledge on the shape of the heart cannot be used. Segmentation of echocardiographic images is challenging because of the low echogenicity of the myocardium in some regions. High resolution information derived from radio frequency (rf) ultrasound data might be a useful additional feature in these regions. A semi-3D technique was used to determine maximum temporal cross-correlation values from the rf-data. To segment the endocardial surface, maximum cross-correlation values were used as additional external force in a deformable model approach and were tested against and combined with adaptive filtered, demodulated rf-data. The method was tested on pediatric full volume images (Philips, iE33) and evaluated by comparison with contours obtained from manual segmentation.