Combined partial differential equation filtering and particle swarm optimization for noisy biomedical image segmentation

Abstract

This paper presents a sequential system to jointly denoise and segment an image contaminated with Gaussian noise. A fourth-order partial differential equation (PDE) filter is used for noise cancelling and particle swarm optimization (PSO) is used for segmentation. The system was tested on a chest X-ray image corrupted with different levels of Gaussian noise and, based on the Jaccard and Dice statistics, the proposed system outperformed nine other hybrid models that denoise and then segment the filtered image.