Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines

Autor: Giovanna Calandra-Buonaura, Giorgio Gnecco, Lia Talozzi, David Neil Manners, Stefania Evangelisti, Rita Morisi, Claudio Bianchini, Pietro Cortelli, Caterina Tonon, Giulia Giannini, Luisa Sambati, Claudia Testa, Laura Ludovica Gramegna, Raffaele Lodi, Nico Lanconelli

Background and purpose In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classif

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfd3f7c61fac5e4ada5f55518d6573f4
http://hdl.handle.net/11585/623913

Supervised and Semi-Supervised Classifiers for the Detection of Flood-Prone Areas

Autor: Giorgio Roth, Rita Morisi, Giorgio Gnecco, Marcello Sanguineti, Angela Celeste Taramasso

Supervised and semi-supervised machine-learning techniques are applied and compared for the recognition of the flood hazard. The learning goal consists in distinguishing between flood-exposed and marginal-risk areas. Kernel-based binary classifiers u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a11cbf157b704db177db92b04651e8c
http://hdl.handle.net/11567/824621

Binary and multi-class parkinsonian disorders classification using support vector machines

Autor: David Neil Manners, Nico Lanconelli, Caterina Tonon, Stefania Evangelisti, Giorgio Gnecco, Pietro Cortelli, Claudio Bianchini, Laura Ludovica Gramegna, Rita Morisi, Raffaele Lodi, Stefano Zanigni, Claudia Testa

Publikováno v: Pattern Recognition and Image Analysis ISBN: 9783319193892
IbPRIA

This paper presents a method for an automated Parkinsonian disorders classification using Support Vector Machines (SVMs). Magnetic Resonance quantitative markers are used as features to train SVMs with the aim of automatically diagnosing patients wit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc02204f07b91b75b3f024d6a1f31713
http://hdl.handle.net/11585/681672

Synthetic Generation of Myocardial Blood-Oxygen-Level-Dependent MRI Time Series via Structural Sparse Decomposition Modeling

Autor: Sotirios A. Tsaftaris, Davide Boschetto, Cristian Rusu, Rita Morisi, Rohan Dharmakumar

Publikováno v: Rusu, C, Morisi, R, Boschetto, D, Dharmakumar, R & Tsaftaris, S A 2014, ' Synthetic generation of myocardial blood-oxygen-level-dependent MRI time series via structural sparse decomposition modeling ', IEEE Transactions on Medical Imaging, vol. 33, no. 7, 6777337, pp. 1422-1433 . https://doi.org/10.1109/TMI.2014.2313000

This paper aims to identify approaches that generate appropriate synthetic data (computer generated) for cardiac phase-resolved blood-oxygen-level-dependent (CP-BOLD) MRI. CP-BOLD MRI is a new contrast agent-and stress-free approach for examining cha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48ce45fb9f53d5aa704212c20d59c4a3
https://europepmc.org/articles/PMC4079741/

Supervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRI

Autor: Nico Lanconelli, Maurizio Bordone, Claudio Lamberti, Bruno Donini, James A. Rosengarten, Dario Turco, Giovana Gavidia, L. Lara, Nick Curzen, Javier Herrero, Cristiana Corsi, Miguel Ángel González Ballester, Eduardo Soudah, Frederic Perez, Rita Morisi, Sergio Vera, John M. Morgan

Publikováno v: Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges ISBN: 9783642369605
STACOM

Delayed Enhancement Magnetic Resonance Imaging can be used to non-invasively differentiate viable from non-viable myocardium within the Left Ventricle in patients suffering from myocardial diseases. Automated segmentation of scarified tissue can be u

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16cb80878db0040a8349facdc152766f
http://hdl.handle.net/11585/134700