• Lucie Lévêque, Cardiff University, UK (main contact point)
  • Hantao Liu, Cardiff University, UK
  • Sabina Baraković, University of Sarajevo, Bosnia and Herzegovina
  • Jasmina Baraković Husić, University of Sarajevo, Bosnia and Herzegovina
  • Asli Kumcu, Ghent University, Belgium
  • Meriem Outtas, INSA Rennes, France
  • António Pinheiro, Universidade de Beira Interior, Portugal
  • Ljiljana Platisa, Ghent University, Belgium
  • Lu Zhang, INSA Rennes, France



Delivering optimal image quality to clinical professionals remains a fundamental challenge for medical imaging technologies, products, and services. Understanding and modeling medical image perception is the key. The research comprises a broad spectrum of aspects related to technical system characteristics, human perception and behavior, user needs and responses, and usability of the delivered content. Being able to control and improve medical image quality requires a detailed understanding of medical image perception.


The goal of this special session is therefore to bring together leading researchers from different disciplines, including medicine (radiology but also other specialties), psychology, neuroscience, computer science, and human-computer interaction to exchange ideas, concepts and approaches, to facilitate discussions and foster new insights into understanding of medical image perception, as well as to promote collaboration and multidisciplinary approaches to medical image quality modelling. We intend to cover timely and challenging subjects, such as (human and computer) detection and discrimination of abnormalities, computer-based perception, the impact of display and ergonomic factors on diagnostic performance, the effect of image processing on perception and performance, and image quality assessment methodologies.

Topics of interest

  • Factors of medical image interpretation
  • Computer-based medical image perception
  • Subjective and objective experiments for medical image quality assessment
  • Relationship between perceptual and task-based medical image quality