Fast Algorithm for Segmentation of Urinary Sediment Microscopic Image

The use of partial differential equations in image processing has become an active area of research in the last few years. In particular, active contours are being used for image segmentation, either explicitly as Snakes, or implicitly through the level set method. The main numerical scheme of these models is based on the simplest finite difference discretization by means of an explicit or Euler-forward scheme. This scheme requires very small time steps in order to be stable. Hence, the whole procedure is rather time-consuming. In this paper, a fast semiimplicit additive operator splitting (AOS) scheme based on the C- V model is presented, which is unconditionally stable, fast, large time step size, and easy to implement. The experimental results for the microscopic image in urinary sediment analysis show that the proposed algorithm is efficient, stable, and convergent and has great application value for automation detection of microscopic image.