Curve-Skeleton Extraction Using Appropriate Threshold Optimization

Abstract

The curve-skeleton of an object is an important abstract geometrical and topological representation of its shape, which is extremely useful for pattern recognition and computer vision applications involving in shape analysis. In this paper, we propose an effective algorithm for extracting curve skeleton based on the definition and properties of curve skeleton from pixel cloud, which integrates the advantages of the visual main parts reliability for object recognition and the skeletons reduced-dimension for object representation. This algorithm can detect each pixel of the image, and find the salience value of each pixel; the salience value is the possibility of the pixel being a skeleton point. Then an appropriate threshold is selected to pruning the skeleton and to get the curve skeleton. In this way, the algorithm can be effective in reducing the number of non-skeleton pixels, and reduce the overall time of extracting skeleton. The experiments show that the skeleton keeps the topology of the target. And the corners of the skeleton are smoother and more natural. In additionally, it can effectively reduce redundant branches of skeleton.

Centerline extraction by neighborhood-statistics thinning for quantitative analysis of corneal nerve fibers

“…Thinning method suffers from excessive erosion, which causes a shortening on the curvilinear structure (Boudaoud et al 2017). Skeletonization produces the centerline with redundant branches (Wang et al 2013). Besides, there also exist other different centerline extraction techniques.…”