Fuzzy bit-plane-dependence image segmentation

This paper presents a novel fuzzy bit-plane-dependence image segmentation methodology. We propose a probability model for characterizing the distributions of image variations based on bit-plane probabilities and dependencies between bit-planes. Compared with the current state-of-the-art image variation models which assume the distributions have specific structures (e.g., symmetry, monotone and periodicity), the proposed model provides a universal parametric representation that can be used to model random distributions without enforcing any specific restrictions on the distributions. In addition, we show that the maximum likelihood estimators of model parameters are joint sufficient statistics, which, in turn, justify the theoretical basis for their use. To effectively segment images with various textures, we propose a fuzzy bit-plane-dependence image segmentation algorithm. The proposed algorithm integrates the bit-plane-dependence probability model into the agglomerative fuzzy algorithm, and incorporates neighboring information and boundary correction for image segmentation applications. Experiments demonstrate the superior performance of the proposed method.