A Global Optimal Image Segmentation Method Using Level Sets
Jia Di · 2005
In this paper a new image segmentation model based on techniques of curve evolution, piecewise smooth Mumford Shah functional for segmentation and level sets was proposed as improvement of C V method. New method shows global optimization and less insensibility of initialization and can detect objects whose boundaries are not necessarily defined by gradient and solves the problem of locating the edges on images with non uniform brightness, for which the previous methods based on piecewise constant Mumford Shah model, including the C V method, are not applicable. Besides, the model was improved for the location of subtle and complicated edges of target objects by the modification of PDE. In order to further stabilize and fasten the level set evolution procedures, the paper addresses an improved approach to construction of the signed distance function using new Voronoi source scanning method, which needs simple comparison and few multiplication operations, faster than the traditional approaches. Finally, various experimental results for synthesized and real images will be presented to prove the proposed model efficiency and stabilized.