Active contour driven by adaptively weighted signed pressure force combined with Legendre polynomial for image segmentation

This paper proposes an active contour driven by adaptively weighted signed pressure force (SPF) combined with the Legendre polynomial method for image segmentation. First, an adaptively weighted global average intensity (GAI) term is defined wherein GAI differences are the weighted factors of the interior and exterior region-driving centers. Second, an adaptively weighted Legendre polynomial intensity (LPI) term is defined which adopts the Legendre polynomial intensity average differences as the weighted factors of the interior and exterior region-driving centers. Finally, the GAI and LPI terms are introduced into a novel SPF function and a coefficient is applied to weight their effect degrees; a new edge stopping function (ESF) is defined and combined with the region-based method to robustly converge the curve to the boundary of the object. Experiments demonstrate that this method is highly accurate and computationally efficient for images with inhomogeneous intensity, blurred edge, low contrast, and noise problems. Moreover, the segmentation results are independent of the initial contour.