Robust DC optimization and its application in medical image processing.

BACKGROUND

Many medical image processing problems can be translated into solving the optimization models. In reality, there are lots of nonconvex optimization problems in medical image processing.

OBJECTIVE

In this paper, we focus on a special class of robust nonconvex optimization, namely, robust optimization where given the parameters, the objective function can be expressed as the difference of convex functions.

METHODS

We present the necessary condition for optimality under general assumptions. To solve this problem, a sequential robust convex optimization algorithm is proposed.

RESULTS

We show that the new algorithm is globally convergent to a stationary point of the original problem under the general assumption about the uncertain set. The application of medical image enhancement is conducted and the numerical result shows its efficiency.