Directional local region-scalable active contour using expandable kernel

This paper extracts another benefit of local region-scalable active contour using expandable kernel (LREK) [1]. We modify it to posses a directional property so that it is capable in detecting objects of desirable edge´s type. Local region-scalable force is determined by pixel´s intensity profile within expandable kernels centered on the contour. While magnitude of intensity difference is used in adaptive local statistics of expandable kernel, sign of the difference is a directional information in controlling the forces to attract the contour only into a particular edge´s type object. By setting a directional parameter, an initial contour placement results in two different segmented outcomes. Formulated via level set, our model is topologically flexible. Local region-scalable information enables our model to converge quickly into desired objects with noises, intensity inhomogeneity, and heterogeneous textures. Moreover, the adaptive local statistics allows in tracing concave boundary with a large capture range.