Investigation on Digital Media Image Processing Algorithm Based on Asynchronous and Inertia Adaptive Particle Swarm Optimization

Abstract

How to keep the original characteristics of the digital media image is quite important in the image segmentation. In this paper, we propose a new algorithm for digital media image segmentation, and it is also can be used in the image processing.The algorithm is based on asynchronous particle swarm optimization algorithm to obtain the adaptive threshold; take the inertia factor into the algorithm, the optimal threshold has been acquired for the image segmentation. Compared with other particle swarm optimization algorithm, the algorithm has the advantages of stable, easy to converge to the optimal solution, and high segmentation speed.

Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm

“…The segmentation simply modifies the image representation by labeling each and every pixel so as to ease the analysis while converting it into something that is more meaningful. Magnetic Resonance Imaging (MRI) unveils high spatial resolution anatomical details involving tissue abnormalities [1]. Brain MRI is specifically employed to unfold the sensitive information related to muscles, ligaments, tendons, nerve damage, bleeding, blood clot, etc., with inbuilt power of distinguishing soft tissues [2].…”

“…Brain MRI is specifically employed to unfold the sensitive information related to muscles, ligaments, tendons, nerve damage, bleeding, blood clot, etc., with inbuilt power of distinguishing soft tissues [2]. MRI segmentation approaches play a vital role in numerous applications in neurology that involve precise estimation of tumor size, tumor location, tumor volume, lesions, blood cells demarcation, therapy, and surgical planning [1,3].…”