IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T21:57:23ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2016-02-26T15:47:40Z2016-03-01T10:41:10Zhttp://eprints.imtlucca.it/id/eprint/3145This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/31452016-02-26T15:47:40ZBinary and
multi-class Parkinsonian disorders classification using Support Vector Machines with
graph-based featuresRita Morisirita.morisi@imtlucca.itGiorgio Gneccogiorgio.gnecco@imtlucca.itNico LanconelliStefano ZanigniDavid Neil MannersClaudia TestaStefania EvangelistiLaura Ludovica GramegnaClaudio BianchiniPietro CortelliCaterina TononRaffaele Lodi2016-02-26T14:35:46Z2016-02-29T08:31:00Zhttp://eprints.imtlucca.it/id/eprint/3129This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/31292016-02-26T14:35:46ZBinary and multi-class classification of parkinsonian disorders with support vector machines based on quantitative brain MR and graph-based featuresLaura Ludovica GramegnaClaudia TestaRita Morisirita.morisi@imtlucca.itStefano ZanigniGiorgio Gneccogiorgio.gnecco@imtlucca.itNico LanconelliDavid Neil MannersStefania EvangelistiPietro CortelliCaterina TononRaffaele Lodi2015-10-19T09:51:47Z2016-04-05T12:19:09Zhttp://eprints.imtlucca.it/id/eprint/2777This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/27772015-10-19T09:51:47ZSupervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRIDelayed 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 used to accurately quantify the percentage of myocardium affected. This paper presents a method for cardiac scar detection and segmentation based on supervised learning and level set segmentation. First, a model of the appearance of scar tissue is trained using a Support Vector Machines classifier on image-derived descriptors. Based on the areas detected by the classifier, an accurate segmentation is performed using a segmentation method based on level sets.Laura LaraSergio VeraFrederic PerezNico LanconelliRita Morisirita.morisi@imtlucca.itBruno DoniniDario TurcoCristiana CorsiClaudio LambertiGiovana GavidiaMaurizio BordoneEduardo SoudahNick CurzenJames RosengartenJohn MorganJavier HerreroMiguel A. González Ballester2015-10-19T09:40:53Z2016-04-06T08:50:40Zhttp://eprints.imtlucca.it/id/eprint/2776This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/27762015-10-19T09:40:53ZBinary and Multi-class Parkinsonian Disorders Classification Using Support Vector MachinesThis 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 with different Parkinsonian disorders. Binary and multi–class classification problems are investigated and applied with the aim of automatically distinguishing the subjects with different forms of disorders. A ranking feature selection method is also used as a preprocessing step in order to asses the significance of the different features in diagnosing Parkinsonian disorders. In particular, it turns out that the features selected as the most meaningful ones reflect the opinions of the clinicians as the most important markers in the diagnosis of these disorders. Concerning the results achieved in the classification phase, they are promising; in the two multi–class classification problems investigated, an average accuracy of 81% and 90% is obtained, while in the binary scenarios taken in consideration, the accuracy is never less than 88%.Rita Morisirita.morisi@imtlucca.itGiorgio Gneccogiorgio.gnecco@imtlucca.itNico LanconelliStefano ZanigniDavid Neil MannersClaudia TestaStefania EvangelistiLauraLudovica GramegnaClaudio BianchiniPietro CortelliCaterina TononRaffaele Lodi2015-10-19T09:31:34Z2015-10-19T09:31:34Zhttp://eprints.imtlucca.it/id/eprint/2775This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/27752015-10-19T09:31:34ZSemi-automated scar detection in delayed enhanced cardiac magnetic resonance imagesLate enhancement cardiac magnetic resonance images (MRI) has the ability to precisely delineate myocardial scars. We present a semi-automated method for detecting scars in cardiac MRI. This model has the potential to improve routine clinical practice since quantification is not currently offered due to time constraints. A first segmentation step was developed for extracting the target regions for potential scar and determining pre-candidate objects. Pattern recognition methods are then applied to the segmented images in order to detect the position of the myocardial scar. The database of late gadolinium enhancement (LE) cardiac MR images consists of 111 blocks of images acquired from 63 patients at the University Hospital Southampton NHS Foundation Trust (UK). At least one scar was present for each patient, and all the scars were manually annotated by an expert. A group of images (around one third of the entire set) was used for training the system which was subsequently tested on all the remaining images. Four different classifiers were trained (Support Vector Machine (SVM), k-nearest neighbor (KNN), Bayesian and feed-forward neural network) and their performance was evaluated by using Free response Receiver Operating Characteristic (FROC) analysis. Feature selection was implemented for analyzing the importance of the various features. The segmentation method proposed allowed the region affected by the scar to be extracted correctly in 96% of the blocks of images. The SVM was shown to be the best classifier for our task, and our system reached an overall sensitivity of 80% with less than 7 false positives per patient. The method we present provides an effective tool for detection of scars on cardiac MRI. This may be of value in clinical practice by permitting routine reporting of scar quantification.Rita Morisirita.morisi@imtlucca.itBruno DoniniNico LanconelliJames RosengardenJohn MorganStephen HardenNick Curzen