Medical image segmentation using a multi-agent system - Seng, W.C.; Chitsaz, M. (2013)
Image segmentation still requires improvements although there have been research works since the last few decades. This is coming due to some issues. Firstly, most image segmentation solutions are problem-based. Secondly, medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested objects. The goal of this work is to design a framework to extract simultaneously several objects of interest from Computed Tomography (CT) images by using some priori-knowledge. Our method used properties of agent in a multi-agent environment. The input image is divided into several sub-images, and each agent works on a sub-image and tries to mark each pixel as a specific region by means of given priori-knowledge. During this time the local agent marks each cell of sub-image individually. Moderator agent checks the outcome of all agentsâ work to produce final segmented image. The experimental results for cranial CT images demonstrated segmentation accuracy around 90.