LIE ANALYSIS

Lie Group Analysis for Medical Image Processing

Coordinatore	TECHNISCHE UNIVERSITEIT EINDHOVEN    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Netherlands [NL]
Totale costo	1˙267˙550 €
EC contributo	1˙267˙550 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2013-StG
Funding Scheme	ERC-SG
Anno di inizio	2014
Periodo (anno-mese-giorno)	2014-01-01   -   2018-12-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	TECHNISCHE UNIVERSITEIT EINDHOVEN  Organization address address: DEN DOLECH 2 city: EINDHOVEN postcode: 5612 AZ contact info Titolo: Dr. Nome: Remco Cognome: Duits Email: send email Telefono: 31402472859 Fax: 31402472859	NL (EINDHOVEN)	hostInstitution	1˙267˙550.00
2	TECHNISCHE UNIVERSITEIT EINDHOVEN  Organization address address: DEN DOLECH 2 city: EINDHOVEN postcode: 5612 AZ contact info Titolo: Mr. Nome: Patrick Cognome: Groothuis Email: send email Telefono: +31 40 247 5165 Fax: +31 40 2436685	NL (EINDHOVEN)	hostInstitution	1˙267˙550.00

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 Obiettivo del progetto (Objective)

'The aim of this project is to substantially improve computer algorithms for image analysis in medical imaging. Currently available techniques often require significant application-specific tuning and have a limited application scope. This is mostly due to the use of non-generic feature spaces that involve many physical dimensions and lack mathematical foundation.

Instead, we derive inspiration from the superior generic pattern recognition capabilities of the human brain and propose a novel operator design aiming at better results and wider applicability. This novel operator design combines (partial and ordinary) differential equations on non-compact Lie groups (induced by stochastic processes and sub-Riemannian geometric control) with wavelet transforms. Many mathematical challenges arise in the analysis and (numerical) solutions of these operators.

The research departs from previously developed insights of the PI on 'invertible orientation scores', which can be regarded as a specific instance in a general Lie group theoretical framework. Within this general framework one obtains a comprehensive invertible score defined on a higher dimensional Lie group beyond position space. The key challenge is to appropriately exploit these scores, their survey of multiple features per position, their underlying group structure, and their invertibility. We will tackle this via left-invariant evolutions and left-invariant sub-Riemannian optimal control within the score.

The orientation score approach will be systematically extended towards multi-scale-and-orientation, multi-velocity and multi-frequency encoding and processing, widening the application scope. Moreover, improvements in contextual enhancement via invertible scores and improvements in optimal curve extractions in the Lie group domain of the score will be pursued. We will develop and apply the resulting algorithms to a wide range of medical imaging challenges in neurological, retinal and cardiac applications.'