Medical Application Using Multi Agent System - A Literature Survey

In this paper we have projected on the involvement of multi-agent system in medical or health care domain. The \ud objective of this study is to provide future researchers more resourceful and focused review of various research \ud papers in this domain. Multi-agent system is most suitable for healthcare paradigm, as the properties of agent \ud based systems deals with heterogeneous multiple agents. Data distribution and data management in a dynamic \ud and distributed environment with multi-user cooperation, made multi-agent system more significant in this field. \ud The disposition of this paper is classified on the basis of theoretical and application approach. We have tried to \ud cover few relevant papers published on last decade. The main aim of this literature survey is to provide a \ud complete road map on multi agent system based research on medical health care platform. \u

Medical Image Segmentation using a Multi- Agent System Approach IAJIT First Online Publication

Abstract: Image segmentation techniques have been an invaluable task in many domains such as quantification of tissue volumes, medical diagnosis, anatomical structure study, treatment planning, etc. Image segmentation is still a debatable problem 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 local 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 subimage individually. Moderator agent checks the outcome of all agents ’ work to produce final segmented image. The experimental results for CT images demonstrated segmentation accuracy around 91 % and efficiency of 7 seconds