Multi-agent medical image segmentation: A survey.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

During the last decades, the healthcare area has increasingly relied on medical imaging for the diagnosis of a growing number of pathologies. The different types of medical images are mostly manually processed by human radiologists for diseases detection and monitoring. However, such a procedure is time-consuming and relies on expert judgment. The latter can be influenced by a variety of factors. One of the most complicated image processing tasks is image segmentation. Medical image segmentation consists of dividing the input image into a set of regions of interest, corresponding to body tissues and organs. Recently, artificial intelligence (AI) techniques brought researchers attention with their promising results for the image segmentation automation. Among AI-based techniques are those that use the Multi-Agent System (MAS) paradigm. This paper presents a comparative study of the multi-agent approaches dedicated to the segmentation of medical images, recently published in the literature.

Authors

  • Mohamed T Bennai
    LIMOSE Laboratory, Faculty of Sciences, University of M'hamed Bougara of Boumerdes, Avenue de l'indépendance, Boumerdes, 35000, Algeria; Université de Reims Champagne Ardenne, CReSTIC EA 3804, Reims 51097, France. Electronic address: m.bennai@univ-boumerdes.dz.
  • Zahia Guessoum
    Université de Reims Champagne Ardenne, CReSTIC EA 3804, Reims 51097, France.
  • Smaine Mazouzi
    Dept. of Computer Science, Université 20 Août 1955, Skikda, Algeria.
  • Stéphane Cormier
    Université de Reims Champagne Ardenne, CReSTIC EA 3804, Reims 51097, France.
  • Mohamed Mezghiche
    LIMOSE Laboratory, Faculty of Sciences, University of M'hamed Bougara of Boumerdes, Avenue de l'indépendance, Boumerdes, 35000, Algeria.