A New Fuzzy Connectedness Relation for Image Segmentation

In the image segmentation field, traditional techniques do not completely meet the segmentation challenges mostly posed by inherently fuzzy images. Fuzzy connectedness and fuzzy clustering are considered as two well-known techniques for introducing fuzzy concepts to the problem of image segmentation. Fuzzy connectedness-based Segmentation methods consider spatial relation of image pixels by "hanging togetherness" a notion based on intensity homogeneity. But, they do not inherently utilize feature information of image pixels. On the other hand, as the segmentation domain of fuzzy clustering-based methods is the feature space they do not consider spatial relations among image pixels. Recently, the authors proposed a new segmentation method based on a combination of fuzzy connectedness and fuzzy clustering called membership connectedness, by which the spatial relation of image pixels is constructed in the related membership domain. In this paper, we have proposed a new fuzzy connectedness relation for image segmentation in membership domain which outperforms the previously defined relation in noisy images. In this relation, we have emphasized on the path length rather than the path strength and have considered shorter paths as more reliable paths in noisy images. Experiments were performed using synthetic as well as brain magnetic resonance image (MRI) datasets. The numerical validation demonstrated the strength of the proposed algorithm especially for medical image segmentation purposes.