Skeletonization of Noisy Images via the Method of Moment

In this paper, we propose a novel approach to robust skeletonization, that is developed based on a statistical method using the Zernike moment theory controlled by Maximum Entropy Principal (MEP). This new concept of skeletonization is articuled into three steps. In the first one, estimation of the underlying probability density function (pdf) using Zernike moment is carried out. In the second, the estimation of optimal pdf is selected using MEP criterion. Finally, the subset of local maxima pixels of the optimal pdf are selected as belonging to the skeleton. This new method is applied on noisy and free noisy binary images. We have tested the proposed Zernike Moment Skeletonization Method (ZMSM) on a variety of real and simulated noisy images, it produces excellent and visually appealing results, with comparison to some well known traditional methods.