Many important applications of image analysis deal with multi-modal images such that each object of interest relates to an individual mode in the marginal signal distribution collected over the image. Segmentation of such seemingly simple images is nonetheless a challenging problem because each meaningful boundary between the objects is rarely formed by easily detectable signal differences (or "edges"). Most commonly, the signals have very close values across the boundary and relate to intersecting tails of distributions describing individual objects. To accurately segment such images, not only the main body but also the tails of each such distribution have to be precisely recovered from the available mixture. We present a re.ned version of our novel EM-based algorithm for accurate unsupervised segmentation of multi-modal grayscale images. It has a considerably improved convergence to a local maximum of the image likelihood and provides a very close approximation of each distribution related to the mode with a linear combination of sign-alternate discrete Gaussian kernels. Experiments with medical images show the proposed segmentation is more accurate than several other known alternatives.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unsupervised Segmentation of Multi-Modal Images by a Precise Approximation of Individual Modes with Linear Combinations of Discrete Gaussians


    Contributors:
    El-Baz, A. (author) / Mohamed, R.M. (author) / Farag, A.A. (author) / Gimel'farb, G. (author)


    Publication date :

    2005-01-01


    Size :

    583561 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Precise acquisition and unsupervised segmentation of multi-spectral images

    Gomez, D. D. / Clemmensen, L. H. / Ersboll, B. K. et al. | British Library Online Contents | 2007


    Unsupervised Segmentation of Road Images

    Rouquet, C. / Bonton, P. | British Library Conference Proceedings | 1997


    Practical mixtures of Gaussians with brightness monitoring

    Atev, S. / Masoud, O. / Papanikolopoulos, N. | IEEE | 2004


    Bounding Multiple Gaussians Uncertainty with Application to Object Tracking

    Zhang, B. / Perina, A. / Li, Z. et al. | British Library Online Contents | 2016


    Unsupervised Multiscale Image Segmentation

    Kam, A. / Fitzgerald, W. / IEEE | British Library Conference Proceedings | 1999