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Automatic active contour segmentation approach via vector field convolution

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Abstract

Among the segmentation methods proposed in the literature, the active contour models have been widely used in medical images segmentation. This is due to their efficiency to capture complex shapes. Nevertheless, the adequate set of the initial curves for active contours is needed to lead to good automatic segmentation results. An adequate initialization method should set the initial active contour model close enough, to the final targeted boundary, to avoid local minima and to improve computational efficiency. In this paper, we present a new approach, based on the divergence of vector field and the Dijkstra’s algorithm to automatically initialize and give the B-Snake the ability to change topology in presence of multiple objects very close to each other. The divergence of vector fields informs about the vectors spread. Negative divergence indicates that the vectors converge and positive divergence indicates that the vectors diverge. Thus, we used the negative region of the divergence of the vector field convolution (VFC) to set the initial active contour near the objects and the positive divergence region was used to split the B-Snake via Dijkstra algorithm. To demonstrate the effectiveness of the proposed method, we use computed tomography (CT) images of close bones. This method gives good segmentation results, especially on CT images presenting proximities, compared to results obtained by other automatic segmentation methods from the literature.

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Acknowledgments

The authors thank Dr. Béchir Abdelmoula of the Ibn Zohr Center of Radiology of Tunis for providing the CT medical image data.

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Authors and Affiliations

  1. Ecole Nationale Supérieure d’Ingénieurs de Tunis, University of Tunis, 5 Av. Taha Hussein Mont Fleury, Tunis, Tunisia

    Houda Bakir, Maher Charfi & Jalel Zrida

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  1. Houda Bakir
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  2. Maher Charfi
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  3. Jalel Zrida
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Correspondence to Houda Bakir.

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Bakir, H., Charfi, M. & Zrida, J. Automatic active contour segmentation approach via vector field convolution. SIViP 10, 9–18 (2016). https://doi.org/10.1007/s11760-014-0695-7

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  • DOI: https://doi.org/10.1007/s11760-014-0695-7

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