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Medical Image Segmentation using a Multi- Agent System Approach

2011
Journal from gdlhub / 2017-08-14 11:52:31
Oleh : Mahsa Chitsaz, Woo Seng, IAJIT
Dibuat : 2012-06-22, dengan 1 file

Keyword : Medical Image Segmentation, Agent, Multi-Agent system.
Subjek : Medical Image Segmentation using a Multi- Agent System Approach
Url : http://www.ccis2k.org/iajit/PDF/vol.10,no.3/3-2999.pdf
Sumber pengambilan dokumen : Internet

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.

Deskripsi Alternatif :

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.

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