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...
| Publicado en: | Technology & Health Care Vol. 29; no. 2; pp. 393 - 406 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. |
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