Automation and acceleration of graph cut based image segmentation utilizing U-net

Abstract

In this paper, we propose automated and accelerated Graph Cut based image segmentation utilizing U-Net. In Graph Cut, seeded image generation is an important element for obtaining highly accurate output images. By utilizing U-Net to automate the generation of seed images, which until now has been done manually, a highly accurate and accelerated Graph Cut are realized. In the simulation, the U-Net is trained from only one original image to generate seeded images for Graph Cut. Using that seeded images, we evaluated the accuracy of test images in which the object and background were segmented by Graph Cut.