Unsupervised hierarchical multi-scale image segmentation level set, wavelet and additive splitting operator

This paper presents an unsupervised hierarchical multi-scale segmentation method for multi-phase images based on a single level set, a multi-scale analysis using wavelets, and the semi-implicit additive operator splitting (AOS) scheme which is stable, fast, and easy to implement The method successively segments image subregions found at each step of the hierarchy using a decision criterion based on the variance of intensity across the current subregion. Each step starts with segmenting a down-sized image, and the solution is mapped back to the original size and used as an initial contour for further processing. While there is some overhead related to processing a down-sized image, there is a substantial speedup in processing the full-sized image and selecting the subimage to be segmented.