A New Algorithm Based on Fuzzy Gibbs Random Fields for Image Segmentation

In this paper a new unsupervised segmentation algorithm based on Fuzzy Gibbs Random Field (FGRF) is proposed. This algorithm, named as FGS, can deal with fuzziness and randomness simultaneously. A Classical Gibbs Random Field (CGRF) servers as bridge between prior FGRF and original image. The FGRF is equivalent to CGRF when no fuzziness is considered; therefore, the FGRF is obviously a generalization of the CGRF. The prior FGRF is described in the Potts model, whose parameter is estimated by the maximum pesudolikelihood (MPL) method. The segmentation results are obtained by fuzzifying the image, updating the membership of FGRF based on maximum a posteriori (MAP) criteria, and defuzzifying the image according to maximum membership principle (MMP). Specially, this algorithm can filter the noise effectively. The experiments show that this algorithm is obviously better than CGRF_ based methods and conventional FCM methods as well.