An improved image segmentation system

In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing bas...

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Permalink: http://skupni.nsk.hr/Record/nsk.NSK01001102361/Details
Matična publikacija: Journal of communications software and systems (Online)
16 (2020), 2 ; str. 143-155
Glavni autori: Allioui, Hanane (Author), Sadgal, Mohamed, El Fazziki, Aziz
Vrsta građe: e-članak
Jezik: eng
Predmet:
Online pristup: https://doi.org/10.24138/jcomss.v16i2.830
Journal of communications software and systems (Online)
Hrčak
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044 |a ci  |c hr 
080 1 |a 004  |2 2011 
080 1 |a 61  |2 2011 
100 1 |a Allioui, Hanane  |4 aut  |9 HR-ZaNSK 
245 1 3 |a An improved image segmentation system  |h [Elektronička građa] :  |b a cooperative multi-agent strategy for 2D/3D medical images /  |c Hanane Allioui, Mohamed Sadgal, Aziz El Fazziki. 
300 |b Ilustr., graf. prikazi. 
504 |a Bibliografija: 43 jed. 
504 |a Summary. 
520 |a In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation process ensuring efficiency. Our results show that the performance indices in the system were higher. Furthermore, the integration of the cooperation paradigm allows to speed up the segmentation process. Besides, the tests reveal the robustness of our method by proving competitive results. Our proposal achieved an accuracy of 93,51%± 0,8, a sensitivity of 93,53%± 5,08 and a specificity rate of 92,64%± 4,01. 
653 0 |a Umjetna inteligencija  |a Inteligentni sustavi  |a Računalne aplikacije  |a Obrada slike  |a Medicinske slike  |a Segmentacija slike 
700 1 |a Sadgal, Mohamed  |4 aut  |9 HR-ZaNSK 
700 1 |a El Fazziki, Aziz  |4 aut  |9 HR-ZaNSK 
773 0 |t Journal of communications software and systems (Online)  |x 1846-6079  |g 16 (2020), 2 ; str. 143-155  |w nsk.(HR-ZaNSK)000644741 
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