Improving image segmentation by gradient vector flow and mean shift

The classical gradient vector flow (GVF) method suffers from deficiency in the presence of other significant edges adjacent to the real boundary. In this paper, we propose an improved energy function to challenge this problem by consistently reducing the Euclidean distance between the inspected centroid of the real boundary and the estimated one of the snake. Experimental work shows the proposed framework outperforms the classical GVF algorithm in different circumstances.