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Segmenting Carotid in CT Using Geometric Potential Field Deformable Model

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Mathematical Methodologies in Pattern Recognition and Machine Learning

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 30))

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Abstract

We present a method for the reconstruction of vascular geometries from medical images. Image denoising is performed using vessel enhancing diffusion, which can smooth out image noise and enhance vessel structures. The Canny edge detection technique, which produces object edges with single pixel width, is used for accurate detection of the lumen boundaries. The image gradients are then used to compute the geometric potential field which gives a global representation of the geometric configuration. The deformable model uses a regional constraint to suppress calcified regions for accurate segmentation of the vessel geometries. The proposed framework shows high accuracy when applied to the segmentation of the carotid arteries from CT images.

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Authors and Affiliations

  1. College of Engineering, Swansea University, Swansea, UK

    Si Yong Yeo, Igor Sazonov & Perumal Nithiarasu

  2. College of Science, Swansea University, Swansea, UK

    Xianghua Xie

Authors
  1. Si Yong Yeo
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  2. Xianghua Xie
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  3. Igor Sazonov
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  4. Perumal Nithiarasu
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Corresponding author

Correspondence to Xianghua Xie .

Editor information

Editors and Affiliations

  1. , Dpt. Lenguajes y Sistemas Informáticos, Jaume I University, Campus del Riu Sec s/n, Castellón de la Plana, 12071, Spain

    Pedro Latorre Carmona

  2. , Dpt. Lenguajes y Sistemas Informáticos, Jaume I University, Campus del Riu Sec s/n, Castellón de la Plana, 12071, Spain

    J. Salvador Sánchez

  3. Technical University of Lisbon, Av. Rovisco Pais, Torre Norte, piso 10, Lisbon, 1049-001, Portugal

    Ana L.N. Fred

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Yeo, S.Y., Xie, X., Sazonov, I., Nithiarasu, P. (2013). Segmenting Carotid in CT Using Geometric Potential Field Deformable Model. In: Latorre Carmona, P., Sánchez, J., Fred, A. (eds) Mathematical Methodologies in Pattern Recognition and Machine Learning. Springer Proceedings in Mathematics & Statistics, vol 30. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5076-4_10

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