A pipeline using multi-layer Tumors Automata for interactive multi-label image segmentation

Sixian Chan, Xiaolong Zhou, Zhuo Zhang, Shengyong Chen

Abstract: In this paper, we investigate a novel algorithm to the problem of interactive image segmentation. We propose an extension of the Growcut framework using the Tumors Automata (TA) formed from the superpixel. The proposed TA is similar to Cellular Automata but can directly deal with superpixel. The superpixels (image segments) can provide powerful boundary cues to guide segmentation, where superpixels can be collected easily by over-segmenting the image using any reasonable existing segmentation algorithms. Given a small number of user-labelled superpixels, the rest of the image is segmented automatically by a TA. When the automaton labels the image, the segmentation evolution is faster than Growcut because of the iterative process. Moreover, a level set method and multi-layer TA are employed to further improve the performance. Experiments conducted on the Berkeley Segmentation Database demonstrate the superior performance of our method over the state-of-the-art methods.