An interesting challenge in e-Health is to develop tools and software in order to benefit the healthcare services. Our applicative context is Magnetic Resonance Imaging (MRI). The main purpose of this paper is to propose a regularization framework for solving an inverse reconstruction problem in MRI. We focus on the Split Bregman method, which is a well known effcient tool for solving a wide variety of opti- mization problems e.g. total variation minimization prob- lems arising from image denoising. The proposed denoising approach, based on the TV/ROF model, involves a second- order derivative penalty term and, accordingly, introduces some modifications to the Split Bregman scheme. Our iterative regularization strategy has interesting features in highlighting the image contrasts and in the noise removal. Numerical experiments prove the goodness of the proposed approach.

A novel split bregman algorithm for MRI denoising task in an e-Health system / Cuomo, Salvatore; Campagna, Rosanna; DE MICHELE, Pasquale; Murano, Aniello; Crisci, S.; Galletti, A.; Marcellino, L.. - 29-:(2016), pp. 1-2. (Intervento presentato al convegno 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2016 tenutosi a grc nel 2016) [10.1145/2910674.2910692].

A novel split bregman algorithm for MRI denoising task in an e-Health system

CUOMO, SALVATORE;CAMPAGNA, ROSANNA;DE MICHELE, PASQUALE;MURANO, ANIELLO;
2016

Abstract

An interesting challenge in e-Health is to develop tools and software in order to benefit the healthcare services. Our applicative context is Magnetic Resonance Imaging (MRI). The main purpose of this paper is to propose a regularization framework for solving an inverse reconstruction problem in MRI. We focus on the Split Bregman method, which is a well known effcient tool for solving a wide variety of opti- mization problems e.g. total variation minimization prob- lems arising from image denoising. The proposed denoising approach, based on the TV/ROF model, involves a second- order derivative penalty term and, accordingly, introduces some modifications to the Split Bregman scheme. Our iterative regularization strategy has interesting features in highlighting the image contrasts and in the noise removal. Numerical experiments prove the goodness of the proposed approach.
2016
9781450343374
9781450343374
A novel split bregman algorithm for MRI denoising task in an e-Health system / Cuomo, Salvatore; Campagna, Rosanna; DE MICHELE, Pasquale; Murano, Aniello; Crisci, S.; Galletti, A.; Marcellino, L.. - 29-:(2016), pp. 1-2. (Intervento presentato al convegno 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2016 tenutosi a grc nel 2016) [10.1145/2910674.2910692].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/661844
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