The Segmentation and Adherence Separation Algorithm of Cotton Fiber

Accurate separation and extraction of natural cotton fiber’s microscopic image are the required prerequisites for the feature analysis of cotton fiber. An edge detection algorithm based on level set combined with clustering idea is proposed as there were no significant differences between target area and background, and each cotton fiber has one lumen. Firstly, the small region is obtained by binarization algorithm, and the outer contour is got by level set algorithm based on it. Then combined the fiber’s outer contour with OSTU, and the rough edge of the fiber is appeared after the burr and broken edge are eliminated. And the seed area of the fiber is emerged by using flooding algorithm and dilation algorithm. Finally, the adherence separation algorithm found on clustering idea is applied to gain the separated fibers. The experimental results show that this algorithm can not only ensure the integrity and the continuity of the fiber’s edge, but also segment each fiber rapidly and reliably.