Comparative evaluation of interactive segmentation approaches

Abstract

Image segmentation is a key technique in image processing with the goal to extract important objects from the image. This evaluation study focuses on the segmentation quality of three different interactive segmentation techniques, namely Region Growing (RG), Watershed (WS) and the cellular automaton based GrowCut (GC) algorithm. Three different evaluation measures are computed to compare the segmentation quality of each algorithm: Rand Index (RI), Mutual Information (MI), and the Dice Coefficient (D). For the images in the publicly available ground truth data base utilized for the evaluation, the GrowCut method has a slight advantage over the other two. The presented results provide insight into the performance and the characteristics with respect to the image quality of each tested algorithm.