A novel model for boundary determination that incorporates prior shape information into geometric active contours is presented. The basic idea of this model is to minimize the energy functional depending on the information of the image gradient and the shape of interest, so that the boundary of the object can be captured either by higher magnitude of the image gradient or by the prior knowledge of its shape. The level set form of the proposed model is also provided. We present our experimental results on some synthetic images, functional MR brain images, and ultrasound images for which the existing active contour methods are not applicable. The existence of the solution to the proposed minimization problem is also discussed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    On the incorporation of shape priors into geometric active contours


    Contributors:
    Yunmei Chen, (author) / Thiruvenkadam, S. (author) / Tagare, H.D. (author) / Feng Huang, (author) / Wilson, D. (author) / Geiser, E.A. (author)


    Publication date :

    2001-01-01


    Size :

    728837 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    On the Incorporation of Shape Priors into Geometric Active Contours

    Chen, Y. / Thiruvenkadam, S. / Tagare, H. et al. | British Library Conference Proceedings | 2001


    Multi-Reference Shape Priors for Active Contours

    Foulonneau, A. / Charbonnier, P. / Heitz, F. | British Library Online Contents | 2009


    Affine-Invariant Multi-reference Shape Priors for Active Contours

    Foulonneau, A. / Charbonnier, P. / Heitz, F. | British Library Conference Proceedings | 2006


    Variational Segmentation of Image Sequences Using Region-Based Active Contours and Deformable Shape Priors

    Fundana, K. / Overgaard, N. C. / Heyden, A. | British Library Online Contents | 2008


    Color and Texture Priors in Active Contours for Model-Based Image Segmentation

    Zhou, Q. / Ma, L. / Chelberg, D. et al. | British Library Conference Proceedings | 2003