Level set issues for efficient image segmentation

Distance mapping possessing computational advantage largely decides the effectiveness of level sets for image segmentation. This article presents the impact of several distance mapping and level set methods suggested in the literature and provides an effective way of handling it. Different distance metric schemes such as the Euclidean, city-block and chessboard distances have been very much prevalent in the literature. This article highlights the use of an effective, fast and efficient distance-mapping technique, i.e. distance mapping using scanning and filling technique, proposed by the authors in their earlier work. Further, this article emphasises the need of periodic reinitialisation of the level set function to a signed distance function which makes curvature term become redundant. As the curve evolves, the level set function loses its signed distance property, leading to reduction in the evolution speed. Frequent reinitialisation of the level set to signed distance function overcomes this limitation and increases the speed of evolution.