2024, Vol. 5, Issue 2, Part A

Image segmentation and edge detection methods have proven to be very useful in a variety of fields, including tissue volume estimation, medical diagnosis, anatomical structure analysis, treatment planning, and so on. Due to several problems, image segmentation remains a contentious subject. To begin with, the majority of image segmentation methods are problem-based. Second, the highly comparable grey levels and textures in medical photos limit most edge detection algorithms. This project aims to provide a framework for extracting several items of interest from CT scans while also utilizing previous information. In a multi-agent context, our technique leverages the characteristics of each agent.

Each local agent divides the input image into sub-images and attempts, using a priori knowledge, to label each pixel as a specific location. During this process, the local agent stamps each cell in the subimago individually. To generate the final segmented image, the moderator agent examines the results of all agents' activities.