Colloquium Computer Science - Prof. Constantinos S. Pattichis, University of Cyprus

Title: AM-FM medical image analysis

Abstract:

Medical image analysis methods require the use of effective representations for differentiating between lesions, diseased regions, and normal structure. Amplitude Modulation - Frequency Modulation (AM- FM) models provide effective representations through physically meaningful descriptors of complex non-stationary structures that can differentiate between the different lesions and normal structure. Based on AM-FM models, medical images are decomposed into AM-FM components where the instantaneous frequency (IF) provides a descriptor of local texture, the instantaneous amplitude (IA) captures slowly varying brightness variations, while the instantaneous phase (IP) provides for a powerful descriptor of location, generalizing the traditionally important role of phase in the Fourier analysis of images. Over the years, AM-FM representations have been used in a wide variety of medical image analysis applications based on a vastly reduced number of features that can be easily learned by simple classifiers. A new model of ultrasound image analysis of atherosclerotic carotid plaque to assess risk of stroke in asymptomatic patients was developed based on multiscale AM-FM analysis using Gabor and Difference of Gaussiasn (DoG) filterbanks. In addition, a new methodology of sparse multiscale AM-FM analysis was developed using Gabor filterbanks as well as multiple Gabor filterbanks. AM-FM component selection criteria and parametrization were introduced to derive discriminatory AM-FM features in predicting asymptomatic vs symptomatic plaque images. The new methodology using Gabor filterbanks achieved better results than texture analysis features. The proposed methodologies provide a new paradigm of AM-FM analysis that opens new horizons in the analysis of medical images towards differentiating between normal and abnormal tissue.

Short bio:

Constantinos S. Pattichis is Professor with the Dep. of Computer Science and Director of the Biomedical Engineering Research Centre at the University of Cyprus and Leader of HealthXR Smart, Ubiquitous, and Participatory Technologies for Healthcare Innovation in the CYENS Centre of Excellence. He has 30 years of experience in eHealth and connected health, medical imaging, biosignal analysis, intelligent systems and explainable AI, and more recently in mHealth interventions based on X Reality applications. He has been involved in numerous projects in these areas funded by EU and other bodies, with a total funding managed in excess of 18 million Euro. He has published 148 journal publications, 253 conference papers, 30 chapters in books and editor of 3 books, 22 journal special issues and 20 conference proceedings in these areas. He is a Fellow of IEEE, IET, International Academy of Medical and Biomedical Engineering (IAMBE) and European Alliance for Medical & Biological Engineering & Science (EAMBES).