A statistical active contour model for interactive clutter image segmentation using graph cut optimization

This paper presents a statistical region based Active Contour Model (ACM) considering the correlation between local and global image statistics to segment cluttered images. Generally, cluttered images do not have constant intensity distribution; rather, the intensity may follow near constant variation in different regions. To quantify this variation, we have considered the Coefficient of Variation (CoV) of the regions interior and exterior to the contour as global statistics and the CoV in the local patches as local statistics. Subsequently, the region energy term of the proposed ACM is designed such that it minimizes the difference between the local and global statistics i.e. it encourages CoV for all the local patches inside and outside of the final contour to be nearly homogeneous. Further, we have verified that the energy formulation can be efficiently discretized and solved using graph cut optimization. The main advantages of graph-based formulation over level set formulation are the existence of a global optimal solution and lesser sensitivity to contour initialization. Additionally, the former formulation is significantly faster being non-iterative or convergable with very few iterations. Experimental results demonstrate the superior performance of our approach against other state-of-the-art active contour approaches and also over its level set counterpart. (c) 2021 Published by Elsevier B.V.