Differential Dynamic Trees for Interactive Image Segmentation

The required number of users’ actions and the response time can critically affect user experience during interactive image segmentation. In this work, we revisit a recent graph-based algorithm, namely Dynamic Trees (DT), which has shown to be more effective than several well-established methods from the literature of graph-based image segmentation. DT solves segmentation by growing optimum-path trees rooted at seed pixels, such that the arc weights are estimated on the fly from image properties of the growing trees, defining the objects as optimum-path forests rooted at their internal seeds. Depending on the application (e.g., 3D medical images), the response time to correct segmentation by adding and removing seeds can seriously compromise the method’s efficiency. We present a differential dynamic trees (DDT) algorithm that adds and removes trees updating optimum paths only in the required regions of the image. We demonstrate that the DDT algorithm can preserve the high effectiveness of DT, being one order of magnitude faster than DT. The experiments also show the advantages of DDT over those well-established counterparts.