Intensity differences between objects are often used for segmentation. In an ideal situation, these differences permit the
computation of edges that form complete contours around objects in the image. However, edges found in real images
are usually a set of real and spurious disconnected boundary segments. Even more challenging are those so called
apparent or subjective contours whose boundary are not defined by intensity or texture variations. In this paper, we
present a novel method to segment and reconstruct images with missing boundaries, including images with large
missing edges commonly found in ultrasound imaging. We test our algorithm on classic synthetic images, phantom
images and on real ultrasound images of the bladder, heart, and colon.