Interactive Image Segmentation Using Level Sets and Dempster-Shafer Theory of Evidence

Abstract. Variational frameworks based on level set methods are popular for the general problem of image segmentation. They combine different feature channels in an energy minimization approach. In contrast to other popular segmentation frameworks, e.g. the graph cut framework, current level set formulations do not allow much user interaction. Except for selecting the initial boundary, the user is barely able to guide or correct the boundary propagation. Based on Dempster-Shafer theory of evidence we propose a segmentation framework which integrates user interaction in a novel way. Given the input image, the proposed algorithm determines the best segmentation allowing the user to take global influence on the boundary propagation.

• Type: Journal
• Year: 2011
• Where: SCIA
• Authors: Björn Scheuermann, Bodo Rosenhahn
• Keywords: Image Analysis, Image Segmentation, Initial Boundary, SCIA 2011, Shafer Theory