Nature-Inspired Algorithms and Applications

9Optimization Techniques for Removing Noise in Digital Medical Images

D. Devasena*, M. Jagadeeswari, B. Sharmila and K. Srinivasan

Department of EIE & ECE, Sri Ramakrishna Engineering College, Coimbatore, India

Abstract

Nowadays, image processing algorithms use optimization to find the best solution in some criteria. Optimization is one, which is used to optimize a required solution with minimum error. The evolutionary computation methods are based on derivative free methods. Which uses the objective function to find the best solution. There are different types of evolutionary algorithm which includes Particle Swarm Optimization (PSO), Bat (BA) method, Fire Fly (FF) method, Social Spider Optimization (SSO), Collective Animal (CA) behavior, Differential Evolution (DE), Genetic Algorithms (GA), and Bacterial Forging Algorithm (BFA). Thus, these evolutionary algorithms addresses the real-time image processing problems.

The removal of noise in medical and satellite images must be very accurate, because during noise removal, details in a medical image embedded with diagnostic information should not be destroyed. The visual quality of the medical image is reduced, and it results in complicate diagnosis and treatment. In satellite images, the analysis and classification becomes harder when its noise is treated. By using optimization techniques the visual perception of the images are improved.

Keywords: Image denoising, evolutionary algorithms, medical imaging, particle swarm optimization, ...