Oriented Relative Fuzzy Connectedness: Theory, Algorithms, and Applications in Image Segmentation

Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help to alleviate part of this problem. In this work, we discuss how to incorporate this property in the Relative Fuzzy Connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named Oriented Relative Fuzzy Connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show the obtained gains in accuracy using medical images of MRI and CT images of thoracic studies.