A Survey on Regional Level Set Image Segmentation Models based on the Energy Functional Similarity Measure

Abstract Image segmentation is an important field of computer vision and has attracted significant research attention in the recent years. In this paper, we provide a survey of regional level set image segmentation models based on the energy functional similarity measure. Our survey begins with an introduction to region-based level set image segmentation and an overview of its general steps. Then the different segmentation models are summarized. We define and survey six categories of regional level set image segmentation models based on energy functional similarity measures. For every category, we present the mainstream approaches from the literature as examples. Experimental analyses are conducted to compare the segmentation performance of various methods, which allow us to draw meaningful conclusions about their mutual advantages and disadvantages. Finally, we conclude this survey by highlighting several promising directions which need to be further explored by the research community in the future.