A Cooperative Approach Based on Local Detection of Similarities and Discontinuities for Brain MR Images Segmentation
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IMAGE & SIGNAL PROCESSING
A Cooperative Approach Based on Local Detection of Similarities and Discontinuities for Brain MR Images Segmentation Mohamed T. Bennai1,2
´ · Smaine Mazouzi3 · Zahia Guessoum2 · Mohamed Mezghiche1 · Stephane Cormier2
Received: 15 May 2020 / Accepted: 15 July 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper introduces a new cooperative multi-agent approach for segmenting brain Magnetic Resonance Images (MRIs). MRIs are manually processed by human radiology experts for the identification of many diseases and the monitoring of their evolution. However, such a task is time-consuming and depends on expert decision, which can be affected by many factors. Therefore, various types of research were and are still conducted to automate MRI processing, mainly MRI segmentation. The approach presented in this paper, without any parametrization or prior knowledge, uses a set of situated agents, locally interacting to segment images according to two main phases: the detection of discontinuities and the detection of similarities. An implementation of this approach was tested on phantom brain MR images to assess the results and prove its efficiency. Experimental results ensure a minimum of 89% Dice coefficient with increasing values of the noise and the intensity non-uniformity. Keywords Image segmentation · Discontinuities · Similarities · Multi-agent systems · Interaction · Cooperation
Introduction During the last decades, many kinds of research have been carried out to integrate information and communication technologies in healthcare. The aim is to support practitioners in their work while ensuring patients’ well-being and safety. Different systems, distributed in several services,
This article is part of the Topical Collection on Healthcare Intelligent Multi-Agent Systems (HIMAS2020) Guest Editors: Neil Vaughan, Sara Montagna, Stefano Mariani, Eloisa Vargiu and Michael I. Schumacher We would like to grant the Algerian General Directorate for Scientific Research and Technological Development our thanks for funding our future research. Mohamed T. Bennai
[email protected] 1
LIMOSE Laboratory, Faculty of Sciences, University of M’hamed Bougara of Boumerdes, Avenue de l’ind´ependance, 35000, Boumerdes, Algeria
2
CReSTIC EA 3804, Universit´e de Reims Champagne Ardenne, Reims, France
3
Department of Computer Science, University 20 Aoˆut 1955, Skikda, Algeria
providing different types of data, must cooperate to achieve a common goal. This situation presents a relevant context for using a Multi-Agent System (MAS). In the literature, a MAS is defined as a population of autonomous software entities called agents, situated in some environment, and that are capable of performing actions in this environment in order to meet their local objectives and the global purpose of the system [4, 36, 37]. Several multi-agent approaches have been proposed in different healthcare areas [9] (assisted living, diagnostic, physiological tele-monitoring . . . ). Ag
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