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Fast and accurate circle tracking using active contour models

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Abstract

In this paper, we deal with the problem of circle tracking across an image sequence. We propose an active contour model based on a new energy. The center and radius of the circle is optimized in each frame by looking for local minima of such energy. The energy estimation does not require edge extraction, it uses the image convolution with a Gaussian kernel and its gradient which is computed using a GPU–CUDA implementation. We propose a Newton–Raphson type algorithm to estimate a local minimum of the energy. The combination of an active contour model which does not require edge detection and a GPU–CUDA implementation provides a fast and accurate method for circle tracking. We present some experimental results on synthetic data, on real images, and on medical images in the context of aorta vessel segmentation in computed tomography (CT) images.

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Authors and Affiliations

  1. Departamento de Informática y Sistemas, CTIM: Centro de tecnologías de la Imagen, Universidad de Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Pamas, Spain

    Carmelo Cuenca, Esther González, Agustín Trujillo, Julio Esclarín, Luis Mazorra & Luis Alvarez

  2. Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Santiago de Compostela, Spain

    Juan Antonio Martínez-Mera & Pablo G. Tahoces

  3. University Hospital Complex of Santiago de Compostela (CHUS), Santiago de Compostela, Spain

    José M. Carreira

Authors
  1. Carmelo Cuenca
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  2. Esther González
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  3. Agustín Trujillo
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  4. Julio Esclarín
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  5. Luis Mazorra
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  6. Luis Alvarez
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  7. Juan Antonio Martínez-Mera
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  8. Pablo G. Tahoces
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  9. José M. Carreira
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Corresponding author

Correspondence to Luis Alvarez.

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Cuenca, C., González, E., Trujillo, A. et al. Fast and accurate circle tracking using active contour models. J Real-Time Image Proc 14, 793–802 (2018). https://doi.org/10.1007/s11554-015-0531-5

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  • DOI: https://doi.org/10.1007/s11554-015-0531-5

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