Density-driven fuzzy connectedness for image segmentation

In the traditional fuzzy connectedness (FC) method, the notion of “hanging togetherness” of image elements specified by their fuzzy connectedness is presented sufficiently. However, the segmentation performance is largely determined by the specified fuzzy affinity; and the FC method generally has disadvantages such as sensitive to the noise, difficult to determine an appropriate threshold in the case of multiple seeds version, etc. While these defects can be overcome by our method, in which the density properties of image elements are taken into account, and each spel can be characterized by a Neighborhood Density Index (NDI). Based on NDI, a novel way to capture the global fuzzy connectedness is proposed, and related algorithms for fuzzy object extraction are presented. In the paper, detailed evaluations and analysis are made about the segmentation results returned by the proposed algorithms and algorithms of the FC method. Extensive experiments and comparisons are conducted to demonstrate the utility of such novel approach.