Grants and Prizes – George Azzopardi, Prof

Research grants

2024	NWO-OTP - Partners with UMCG. Topic: Video analysis for the diagnosis of hyperkinetic movement disorders. PhD candidate: Rafael Martínez García Peña
2023	DSSC XS fund jointly received with with Dr. Andreas Milias Argeitis - Achieving robustness to distribution shifts in instance segmentation of time-lapse microscopy: A brain-inspired inhibition approach
2023	Funding by RDW for the research and development of algorithms to prevent face morphing attacks
2022	CogniGron joint funding with Prof. Michael Biehl for two complementary PhD projects with the overarching title: Robust Learning of Sparse Representations: Brain-inspired Inhibition and Statistical Physics Analysis
2022	Faculty internal fund for a full-time PhD project. Open PhD position on the topic: The role of brain-inspired push-pull inhibition phenomenon in deep learning.
2021	University Internal fund for a PhD sandwich position in collaboration with the Stellenbosch University and Astron. Topic: Computer-aided tools for the analysis of large scale radio-astronomy data. PhD candidate: Steven Machetho
2020	Individual - Gratama Foundation, University of Groningen - Topic: Blockchain-based framework for managing the chain of digital custody. Research assistant: Derrick Timmerman
2020	Internal fund from the Data Science & Systems Complexity (DSSC), Unviersity of Groningen. It is in collaboration with Prof. Ben Giepmans (UMCG). Topic: Segmentation of large-scale electron microscopy (EM) 2D images. PhD candidate: Anusha Aswath
2018	Consortium-based - EU Internal Security Fund: WP leader of 4NSEEK - PhD candidate: Guru Bennabhaktula
2014	NWO-STW - Partners with Wageningen University. Topic: Smart breeding with advanced machine learning. PhD candidate: Ahmad Alsahaf

Prize

2024

Winner of ENLIGHT Impact Award

2023

Ben Feringa Impact Award

2015

Best Paper Award at the VIPImage 2015 Conference, Tenerife, Spain.

Strisciuglio, Nicola; Vento, Mario; Azzopardi, George; Petkov, Nicolai

Unsupervised delineation of the vessel tree in retinal fundus images Proceedings Article

In: Tavares, Joao Manuel R. S.; Jorge, R. M. Natal (Ed.): Computational Vision and Medical Image Processing V - Proceedings of 5th Eccomas Thematic Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, pp. 149-156, 2015, ISBN: 9781138029262.

Abstract | Links | BibTeX

@inproceedings{strisciuglio2015unsupervised,

title = {Unsupervised delineation of the vessel tree in retinal fundus images},

author = {Nicola Strisciuglio and Mario Vento and George Azzopardi and Nicolai Petkov},

editor = {Joao Manuel R.S. Tavares and R.M. Natal Jorge},

url = {https://research.rug.nl/en/publications/unsupervised-delineation-of-the-vessel-tree-in-retinal-fundus-ima},

isbn = {9781138029262},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

booktitle = {Computational Vision and Medical Image Processing V - Proceedings of 5th Eccomas Thematic Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing},

pages = {149-156},

abstract = {Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. RE filter to be configured by the automatic analysis of any given vessel-like pattern. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding. We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE_DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}