Journal article Closed Access

Variational segmentation of vector-valued images with gradient vector flow.

Jaouen, Vincent; González, Paulo; Stute, Simon; Guilloteau, Denis; Chalon, Sylvie; Buvat, Irène; Tauber, Clovis


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        <foaf:name>Tauber, Clovis</foaf:name>
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    <dct:title>Variational segmentation of vector-valued images with gradient vector flow.</dct:title>
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    <dcat:keyword>deformable models</dcat:keyword>
    <dcat:keyword>dynamic PET</dcat:keyword>
    <dcat:keyword>gradient vector flow</dcat:keyword>
    <dcat:keyword>structure tensor</dcat:keyword>
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    <dct:description>&lt;p&gt;In this paper, we extend the gradient vector flow&lt;/p&gt; &lt;p&gt;field for robust variational segmentation of vector-valued images.&lt;/p&gt; &lt;p&gt;Rather than using scalar edge information, we define a vectorial&lt;/p&gt; &lt;p&gt;edge map derived from a weighted local structure tensor of&lt;/p&gt; &lt;p&gt;the image that enables the diffusion of the gradient vectors in&lt;/p&gt; &lt;p&gt;accurate directions through the 4D gradient vector flow equation.&lt;/p&gt; &lt;p&gt;To reduce the contribution of noise in the structure tensor,&lt;/p&gt; &lt;p&gt;image channels are weighted according to a blind estimator of&lt;/p&gt; &lt;p&gt;contrast. The method is applied to biological volume delineation&lt;/p&gt; &lt;p&gt;in dynamic PET imaging, and validated on realistic Monte Carlo&lt;/p&gt; &lt;p&gt;simulations of numerical phantoms as well as on real images.&lt;/p&gt;</dct:description>
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