Color image segmentation by combining the convex active contour and the Chan Vese model

In this paper, we present a robust and computationally efficient image segmentation technique based on a hybrid convex active contour and the Chan–Vese (CV) model. The proposed algorithm overcomes the drawbacks of existing image segmentation techniques which are heavily dependent upon the initial user input. Here, we propose to combine region-based and boundary-based techniques for segmentation so that we guarantee robustness across all types of images. We start with a either a geodesic-based or a dynamic region merging (DRM)-based contour before using the CV model. Contrary to the basic geodesic model, the random walk technique, and the snake-based convex active contour model, our algorithm works with minimal input and is shown to be independent of the location of the input pixels provided by the user. The algorithm works by initiating a contour which is either based on the geodesic distance or the DRM model. This contour is then used with the CV model to further refine the segmentation results. We tested the proposed algorithm on several standard databases using both subjective and objective measures. Our experimental results show that the proposed algorithm outperforms recently proposed approaches over indoor and outdoor images in terms of both processing time and segmentation accuracy.