Tensor fields specifically, matrix valued data sets, have recently attracted increased attention in the fields of image processing, computer vision, visualization and medical imaging. In this paper, we present a novel definition of tensor "distance" grounded in concepts from information theory and incorporate it in the segmentation of tensor-valued images. In some applications, a symmetric positive definite (SPD) tensor at each point of a tensor valued image can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between SPD tensors would be the KL divergence or its relative. We propose the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the tensors being compared that leads to a novel closed form expression. Unlike the traditional Frobenius norm-based tensor distance, our "distance" is affine invariant, a desirable property in many applications. We then incorporate this new tensor "distance" in a region based active contour model for bimodal tensor field segmentation and show its application to the segmentation of diffusion tensor magnetic resonance images (DT-MRI) as well as for the texture segmentation problem in computer vision. Synthetic and real data experiments are shown to depict the performance of the proposed model.


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    Titel :

    An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation


    Beteiligte:
    Zhizhou Wang, (Autor:in) / Vemuri, B.C. (Autor:in)


    Erscheinungsdatum :

    01.01.2004


    Format / Umfang :

    754403 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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