Recent Trends in Biomedical Technologies: Challenges and Opportunities

2. Biomedical Image Processing

Most current methods are unique, and it seems that some modifications are required to utilize them in other situations. Some methods for organizing machine vision require a significant amount of computer power. The article focuses on methods to enhance the quality of pattern recognition. The suggested paper's relevance to the computer vision field is explained by the fact that computer vision may be integrated in vehicles, drones, and utilized to automate processes like road traffic and space monitoring. It is critical to offer accurate computer vision work since mistakes may result in fatalities and damages.

The authors claim that their goal is to develop efficient image processing techniques in order to prevent such outcomes. Segmentation and clustering are two terms that may be used to describe them. They do not, however, guarantee reliable findings in the event of visual information distortion or a high degree of noise. The reason for this is because these techniques need manual tweaking. The paper's authors attempted to create a clustering technique based on particle swarm optimization (PSO) that automatically selects parameters. This technique also incorporates the k-means clustering method, which calculates the minimum value of the distance function to arrange pixels into a predefined number of clusters. The particle group motion (pixel-by-pixel transit across the picture) and search for the optimum solution for the whole swarm (search for pixel with the highest average intensity value in a specific area) are utilized in comparison to the PSO.