Vascular tree object segmentation by deskeletonization of valley courses.

In this paper, we propose a valley-course-based image segmentation technique for tree-like object delineation, as an alternative to the traditional centerline-based methods. This technique consists of valley-course extraction, skeleton pruning and deskeletonization. Valley courses, constructed from valley points that are obtained by star-pattern scanning over an image, offer a natural manner of identifying tree skeletons. Unattached segments are removed using morphological operations. A structured tree is then constructed from the skeletons by using a tree pruning/spanning algorithm. A fleshy tree-like object is obtained by a deskeletonization procedure, which consists of extracting tree boundary in vicinity of the skeletons in the original image. The tree boundary is determined by identifying paired edge points at a valley point. A derivative-free edge identification approach is proposed, which defines an edge point at a side-slope by a relative intensity drop with respect to the local background. An empirical formula using a logarithmic function of local intensity contrast offers desirable characteristics of adaptability and stability. The adaptability of edge points to the local background is attributed to the compression behavior of logarithmic function. Furthermore, stability to noise is resulted because derivative operations are not used. The segmentation technique was validated using coronary angiographic images.