Medical Image Segmentation using a Multi-Agent System Approach

Mahsa Chitsaz and Woo Seng
Faculty of Computer Science and Information Technology, University of Malaya, Malaysia

 
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 sub-image 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.

Keywords: Medical Image Segmentation, Agent, Multi-Agent system.

 

Received June 9, 2010; accepted March 1, 2011

  

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